Relative predictive utility of the original and Autism-Specific Five-Minute Speech Samples for child behaviour problems in autistic preschoolers: A preliminary study

Abstract

Many autistic children have co-occurring behavioural problems influencing core autism symptomology potentially relevant for intervention planning. Parental Expressed Emotion – reflecting critical, hostile and overprotective comments – contributes to understanding and predicting behaviour in autistic school-aged children, adolescents and adults and is typically measured using the Five-Minute Speech Sample. However, limitations exist for its use with parents of younger autistic children and so the Autism-Specific Five-Minute Speech Sample was adapted with the goal of better measuring parent Expressed Emotion in the context of childhood autism. The Autism-Specific Five-Minute Speech Sample has not yet been used to explore Expressed Emotion in parents of autistic preschoolers, nor has the relative predictive utility of the Autism-Specific Five-Minute Speech Sample and Five-Minute Speech Sample been evaluated in the same sample. We compared the two measures from speech samples provided by 51 Australian parents with newly diagnosed autistic preschoolers, including investigating their predictive value for concurrent and subsequent child internalising and externalising behaviour problems. While Autism-Specific Five-Minute Speech Sample Expressed Emotion and Five-Minute Speech Sample Expressed Emotion were associated in this sample, only Autism-Specific Five-Minute Speech Sample codes contributed significant predictive value for concurrent and subsequent child problem behaviour. These preliminary data strengthen the position that the Autism-Specific Five-Minute Speech Sample may better capture Expressed Emotion, than the Five-Minute Speech Sample, among parents of autistic preschool-aged children.

Lay abstract

Parental Expressed Emotion refers to the intensity and nature of emotion shown when a parent talks about their child, and has been linked to child behaviour outcomes. Parental Expressed Emotion has typically been measured using the Five-Minute Speech Sample; however, the Autism-Specific Five-Minute Speech Sample was developed to better capture Expressed Emotion for parents of children on the autism spectrum. In each case, parents are asked to talk for 5 min about their child and how they get along with their child. Parents’ statements are then coded for features such as number of positive and critical comments, or statements reflecting strong emotional involvement. While both the Five-Minute Speech Sample and Autism-Specific Five-Minute Speech Sample have been used with parents of autistic school-aged children, their relative usefulness for measuring Expressed Emotion in parents of preschool-aged children – including their links to child behaviour problems in this group – is unclear. We collected speech samples from 51 parents of newly diagnosed autistic preschoolers to investigate similarities and differences in results from the Five-Minute Speech Sample and Autism-Specific Five-Minute Speech Sample coding schemes. This included exploring the extent to which the Five-Minute Speech Sample and Autism-Specific Five-Minute Speech Sample, separately, or together, predicted current and future child behaviour problems. While the two measures were related, we found only the Autism-Specific Five-Minute Speech Sample – but not the Five-Minute Speech Sample – was related to child behavioural challenges. This adds support to the suggestion that the Autism-Specific Five-Minute Speech Sample may be a more useful measure of parental Expressed Emotion in this group, and provides a first step towards understanding how autistic children might be better supported by targeting parental Expressed Emotion.

Keywords

Autism-Specific Five-Minute Speech Sample Expressed Emotion externalising and internalising behaviour parent–child interaction preschoolers reliability validity

Co-occurring social-emotional and behavioural problems are common in autistic children (Dovgan & Mazurek, 2019; Totsika et al., 2011). In one group of school-aged autistic children (Mage = 10.91 years, standard deviation (SD) = 3.34), Hoffmann et al. (2016) found clinically significant internalising and externalising behaviours in 70.8% and 41.5% of children, respectively. The presence of co-occurring behaviour difficulties likely influences the complexity of core autism characteristics (Waters & Healy, 2012), and may increase the likelihood of children receiving inappropriate interventions and/or being excluded from services (Horner et al., 2002; Waters & Healy, 2012). Effective intervention planning requires understanding of factors that may be associated with behaviour difficulties. Given its relation to child behavioural difficulties, parental Expressed Emotion (EE) – characterised by critical, hostile and/or overprotective comments about one’s child – is one construct that has received attention in paediatric research, including in the context of childhood autism (Baker et al., 2019; Benson et al., 2011; Psychogiou et al., 2017; Romero-Gonzalez et al., 2018).

Recently published systematic reviews and meta-analyses with autistic and non-autistic cohorts have found the strongest associations between EE criticism and internalising and externalising behaviours (see Rea et al., 2020; Romero-Gonzalez et al., 2018), with criticism most associated with externalising behaviour, including in families of younger autistic children (Mage = 6.48 years, range = 4–11 years; Baker et al., 2019). Notwithstanding the growing body of research, understanding about relationships between parental EE and child behavioural outcomes in the context of newly diagnosed autism is limited – despite unique factors existing for families during this period likely to influence parent EE (i.e. diagnosis acceptance, navigating services systems and/or commencing time-consuming interventions; Green et al., 2021). Knowing more about the potential relevance of parent EE for the development of behaviour difficulties among newly diagnosed preschoolers may provide insights into how best to support early family dynamics to optimise outcomes for parents and children alike.

EE measurement

The Five-Minute Speech Sample (FMSS) is commonly used in empirical research to quantify parental EE from an unguided 5-min speech recording (Magaña et al., 1986; Magaña-Amato, 1993). Following the original FMSS coding scheme (Magaña-Amato, 1993), high EE is characterised by either/both high criticism (i.e. criticism of the child/their relationship accompanied by negative emotion), or high emotional over-involvement (EOI; i.e. over-protectiveness, self-sacrifice, overt displays of emotion, excessive detail about the past, statements of love and excessive praise). The FMSS (Magaña et al., 1986; Magaña-Amato, 1993) is based on, and has been validated against an original longer interview-based EE measure (i.e. the Camberwell Family Interview (CFI); Brown & Rutter, 1966; Vaughan & Leff, 1976) which was developed to capture EE in families of adults with schizophrenia. However, despite ubiquitous use of the FMSS, including with parents reporting on infant- and toddler-aged children (i.e. Sher-Censor et al., 2018; Sher-Censor & Yates, 2015), concerns have been raised regarding its use with parents of young children.

In particular, the appositeness of the FMSS EOI criteria for child populations has been questioned, especially regarding over-protectiveness and statements of love/praise. As an illustration, agreeing upon what constitutes parental over-protectiveness in the early years – that is, differentiating what is helpful versus excessive support – is problematic (Daley et al., 2003; Rea et al., 2020), especially in the context of childhood disability where child dependency on parental care is functionally appropriate (Benson et al., 2011). Statements of love/praise about young children are highly likely in this cohort (Daley et al., 2003; Wamboldt et al., 2000) and positive maternal comments are negatively associated with children’s emotional and behaviour problems at this age (Psychogiou et al., 2017). Consequently, there may be less foundation on which to base decisions to conceptualise excessive Positive Remarks (when ⩾5) as a negative indicator of parental EE. Similarly, it has been suggested that parents are unlikely to overtly criticise preschool-aged children (Daley et al., 2003) which may impact the ability of the original FMSS to sensitively capture parental criticism. Furthermore, a global judgement about parental warmth – a component of the CFI (Brown & Rutter, 1966; Vaughan & Leff, 1976) – is not coded in the original FMSS scheme, although has been included by several researchers as a supplementary code (see Hickey et al., 2019, 2020; Orsmond et al., 2006). Given that parental warmth is likely to foster early child development (Baker et al., 2019; Midouhas et al., 2013), there is an argument that, alongside positive comments, the inclusion of warmth as a distinct component of parent EE is important.

Adaptations of the original FMSS coding scheme have been made to increase suitability for use with parents reporting on young children. For example, the Preschool Five-Minute Speech Sample (PFMSS; Daley et al., 2003) tailored existing categories (i.e. EOI and Relationship) and included additional, standalone categories important for early development (i.e. Warmth and Positive Comments), with research indicating that the PFMSS is a valid and reliable measure of parental EE in preschool populations (Daley et al., 2003; Psychogiou et al., 2017). More recently, the Autism-Specific Five-Minute Speech Sample (AFMSS; Daley & Benson, 2008) adaptation has sought to increase the appositeness of this tool for use with parents of younger autistic children. Given that many of the broad PFMSS adaptations have been carried into the AFMSS adaptation – including Warmth as a standalone code, and the disentangling of Positive Comments from the EOI code – our focus hereafter is restricted to distinctions between FMSS and AFMSS coding.

AFMSS

To classify EE in the AFMSS coding system, the six broad PFMSS codes remain (i.e. Initial Statement, Warmth, Relationship, EOI, Critical Comments and Positive Comments), but with calibrated coding conventions for use with parents of autistic preadolescents. As an example, Benson et al. (2011) noted that it is common for parents of autistic children to focus some of their narrative on child symptom improvement and skill acquisition. So, in AFMSS coding, statements about child progress are not rated as positive comments unless said in a positive tone or elaborated upon in an appreciative/affirmative way (e.g. ‘She’s made progress with eating: it’s fantastic’). Likewise, parent statements about child behaviour are not rated as critical comments unless the parent uses a negative tone or reinforces that the behaviour is viewed negatively (e.g. ‘He constantly asks the same question which is so irritating’). Critical comments are thus coded conservatively in the AFMSS scheme – as is true for FMSS coding (Magaña et al., 1986; Magaña-Amato, 1993) so it is likely that criticism would be rare across both schemes. AFMSS Relationship and Initial Statement codes have also been adapted to increase applicability for parents of young autistic children (see Benson et al., 2011). More appreciable differences between FMSS and AFMSS coding are evident on the EOI code. Only evidence of parental comments about the child that demonstrate over-protectiveness/self-sacrifice and lack of objectivity are coded in AFMSS EOI. Overt emotional displays, excessive descriptions about the past, and statements of love and excessive praise do not feature in AFMSS EOI coding.

The validity of the AFMSS was explored by the instrument developers in one US cohort (Benson et al., 2011), a majority Caucasian and highly educated group of mothers of autistic 6- to 9-year olds. In this study, EE was not associated with composite child behaviour problems – only with child social competence – a finding that contradicts the broader current body of literature where high levels of EE are significantly associated with higher levels of child behavioural problems (Romero-Gonzalez et al., 2018). The absence of such an association between indicators of EE and child behaviour problems in Benson et al.’s (2011) study raises questions about the convergent validity of the AFMSS. Yet, more recently, Baker et al. (2019) investigated two discrete AFMSS categories (Warmth and Criticism) in relation to child behaviour problems in autistic children aged 4–11 years. They found associations between AFMSS parental Warmth and Critical Comments with child externalising, but not internalising behaviour problems. Other empirical evidence echoes Baker et al.’s findings whereby high EE, especially criticism, most strongly relates to externalising behaviours (Bolton et al., 2003; McCarty et al., 2004; Peris & Baker, 2000). Further exploration of associations between AFMSS EE – and, more importantly, its subcomponents – and different constructs of child behaviour is warranted to better understand the mechanisms at play in these relationships (Baker et al., 2019).

Current study

The AFMSS was developed to address concerns regarding validity of the original FMSS measure with parents of young, autistic children. However, as the FMSS and AFMSS have not been directly compared with this population, the convergence between these and predictive utility of each for child outcomes has yet to be empirically established. We undertook to directly compare independently coded speech samples collected from parents of autistic preschoolers following the AFMSS and FMSS schemes, and explore the relative predictive value of these ratings for child internalising and externalising behaviours. Since the AFMSS was based on the FMSS, we hypothesised that most analogous AFMSS and FMSS codes would be somewhat associated with one another (i.e. over-arching EE, criticism, and quality of the parent–child relationship) with the exception of FMSS and AFMSS assessments of EOI (i.e. given their more distinct criteria). We also hypothesised that AFMSS codes might better predict later child behaviour than FMSS codes in our sample, given the intentional adaptation of the former to better suit this target population, and particularly concerning externalising behaviour given the recent report of Baker et al. (2019).

Method

Participants and procedure

This study utilised data collected from a larger longitudinal study of autistic children and their families (La Trobe University Human Research Ethics Committee; HREC #16-136). Between 2017 and 2019, 53 parent–child dyads were recruited in Melbourne, Australia. Research assessments were completed at study entry and 5-month follow-up. Children were eligible if aged 17–43 months, walking independently, diagnosed with autism spectrum disorder (ASD) – confirmed through administration of the Autism Diagnostic Observation Schedule-2 (ADOS-2; Lord et al., 2012) by a research-reliable assessor – and had non-verbal developmental age-equivalence ⩾12 months – confirmed through administration of the Mullen Scales of Early Learning (MSEL; Mullen, 1995).

Baseline FMSSs were available for 51 parents (n = 2 missing). Table 1 provides parent and child characteristics. Most children were male with an average age under 3 years. Autism symptom presentation varied substantially, but with moderate-to-significant mean-level presentation. Mean verbal and non-verbal developmental skills were below age-expected levels, with verbal skills significantly lower than non-verbal abilities, t(50) = 5.50, p < 0.001, d = 0.77. Most participating parents were biological mothers and well educated (n = 3 missing). Our sample was otherwise more heterogenous than most others in the field (see Romero-Gonzalez et al., 2018), with a substantial minority subgroup reporting low-income status (n = 3 cases missing) and more than half self-described as culturally and linguistically diverse (with home languages including Mandarin, Cantonese and Tamil).

Table 1.

Baseline participant characteristics.

Factors		N	M/n (SD/%)	Range
Child	Boys	51	43 (84.3)
	Girls		8 (15.7)
	Age (months)	51	34.80 (6.4)	17–44
	MSEL ELC	51	66.37 (23.36)	49–137
	Non-Verbal Developmental Quotient		73.21 (24.14)	32–150
	Verbal Developmental Quotient		59.05 (32.56)	13–130
	ADOS CSS	51	7.12 (1.66)	3–10
	CBCL Internalising	47	17.6 (8.1)	2–34
	CBCL Externalising	47	20.1 (9.0)	4–41
Parent	Mothers	51	44 (86.3)
	Age (years)	46	36 (4.54)	26–48
	DASS Total Score	51	26.28 (17.41)	0–62
Family	Culturally and Linguistically Diverse	51	27 (52.9)
	Low-income status^a	48	16 (31.37)
	Maternal Education	47
	Primary		1 (2.0)
	Secondary		6 (11.8)
	Tertiary		20 (39.2)
	Postgraduate		20 (39.2)

SD: standard deviation; MSEL: Mullen Scales of Early Learning (Mullen, 1995); ELC: Early Learning Composite (Standard Score; population M = 100, SD = 15); ADOS: Autism Diagnostic Observation Schedule (Lord et al., 2012); CSS: Calibrated Severity Score (range = 1–10); CBCL: Child Behavior Checklist (Achenbach & Edelbrock, 1991); DASS: Depression Anxiety Stress Scale (Lovibond & Lovibond, 1995).

Adjusted taxable income below income threshold of AU$37,960, or partner receiving income support.

Baseline assessments were conducted by two researchers at a university-based space. Where two parents attended assessments, the designated primary caregiver provided the speech sample. Assessments took place in a single room with one researcher completing parent assessments (i.e. speech sample and parent-report assessments) and another completing child activities (i.e. developmental and autism assessments).

Key measures

Speech samples

To ensure parental reflections were as unbiased as possible, collection of speech samples was the first task completed at baseline. A researcher read the standardised script from Magaña-Amato (1993):

I’d like to hear your thoughts and feelings about [child] in your own words and without my interrupting with questions or comments. When I ask you to begin, I’d like you to speak for five minutes telling me what kind of person [child] is and how you get along together.

The speech sample was audio-recorded for transcription and coding.

AFMSS coding

Following training by one of the instrument developers (P.R.B.), two researchers coded baseline transcripts according to the AFMSS coding scheme (Benson et al., 2011). The AFMSS scheme includes four global codes: Initial Statement (Positive/Neutral/Negative), Warmth (High/Moderate/Low), Relationship (Positive/Neutral/Negative) and EOI (High/Moderate/Low). Critical Comments and Positive Comments are also tallied. High EE is assigned when any global code is rated negative/low and Critical Comments > Positive Comments. Moderate/borderline EE is assigned when any global code is rated negative/low or when Critical Comments > Positive Comments. Low EE is assigned in all other cases.

Inter-rater reliability for AFMSS coding was evaluated on 16 randomly selected transcripts (31.4%). As per recommendations by Syed and Nelson (2015), two indices of reliability are presented for categorical data to account for the distinct information offered by different measures. Overall, we found good reliability for Initial Statement (κ = 0.75, 88% agreement), Positive Comments (intraclass correlation coefficient (ICC) = 0.92), Critical Comments (ICC = 0.96) and EE (κ = 0.87, 94% agreement). The κ-value was lower than acceptable for Relationship (κ = 0.47, 81% agreement) and Warmth (κ = 0.67, 81% agreement), but overall percentage agreement was acceptable (i.e. ⩾80% agreement; Belur et al., 2018).

FMSS coding

Another researcher, not trained in AFMSS coding, received training on the FMSS coding scheme (Magaña et al., 1986) by an accredited FMSS trainer (J.B.; Barnes McGuire & Earls, 1994) and coded all transcripts. Codes for FMSS Initial Statement (Positive/Neutral/Negative), Relationship (Positive/Neutral/Negative), Criticism (frequency count) and Dissatisfaction (present/absent) are combined for an over-arching Criticism code (High/Borderline/Low). An over-arching EOI code (High/Borderline/Low) is based on Emotional Display (i.e. crying/choking up; present/absent), Statements of Attitude (expressing love for child and frequency count), Self-Sacrificing/Overprotective Behaviour (present/absent), Excessive Detail about child at a younger age (present/absent) and excessive Positive Remarks (frequency count). High EE is characterised by high Criticism (Negative Initial Statement, Negative Relationship or ⩾1 criticism) and/or high EOI (Self-Sacrificing/Over-protectiveness, Emotional Displays, or ⩾2 ratings of Excessive Detail, expressions of love, or ⩾5 Positive Remarks). Borderline EE is coded if only one of the EOI codes is present, or if only Dissatisfaction is present from the critical EOI codes (see Sher-Censor, 2015 for discussion of Borderline ratings).

Inter-rater reliability was evaluated on 10 (19.6%) randomly selected transcripts (i.e. not those used in training) with J.B. coding inter-rater samples. Good reliability was achieved for FMSS Criticism (κ = 0.92, 83% agreement), EOI (κ = 0.78, 92% agreement) and EE (κ = 0.82, 92% agreement), and subcomponents of Initial Statement (κ = 0.80, 75% agreement), Relationship (κ = 0.79, 75% agreement), Dissatisfaction (κ = 0.92, 83% agreement), Emotional Displays (κ = 1.00, 100% agreement) and Positive Remarks (κ = 0.82, ICC = 0.91). Lower than acceptable κ-statistics were reported for Self-Sacrifice (κ = 0.49, 92% agreement), Excessive Detail (κ = 0.63, 92% agreement) and Statements of Attitude (κ = 0.57, 83% agreement), but raw agreement was good across all codes.

Internalising and externalising behaviours

Child internalising and externalising behaviour problems were assessed at baseline and 5-month follow-up, using the parent–report Child Behavior Checklist (CBCL, 1.5–5-year version; Achenbach & Rescorla, 2001) which provides domain scores for internalising/emotional problems (e.g. anxiety and emotional reactivity) and externalising/behavioural problems (e.g. hyperactivity and aggression). Higher scores reflect greater difficulty. The caregiver-report CBCL has adequate sensitivity (0.66) and strong specificity (0.83) for detecting behaviour problems in clinical and community samples (Warnick et al., 2008), including in young autistic children (Medeiros et al., 2017).

Covariates

Potential covariates considered for inclusion in analyses included child sex, the highest level of maternal education (dichotomised, combining Primary, Secondary and Tertiary vs Postgraduate qualification), family low versus all other income status, child autism severity (derived from ADOS-2 Calibrated Severity Scores (CSSs), range = 1–10; Lord et al., 2012), child cognitive ability (using the MSEL Early Learning Composite, range = 49–155; Mullen, 1995) and parental psychopathology (using the 21-item version of the Depression Anxiety Stress Scale (DASS) total score, range = 0–120; Lovibond & Lovibond, 1995).

Analysis

We first examined consistency across comparable codes for the AFMSS and FMSS schemes using the chi-square tests (ordinal variables) and t-tests and correlations (continuous variables). Associations between baseline AFMSS and FMSS codes, and concurrent and subsequent CBCL scores were then investigated using analysis of variances (ANOVAs) (categorical factors) or Spearman’s Rank-Order Correlations (continuous factors). Finally, we included those factors showing significant association with CBCL scores within hierarchical regression analyses. We examined the predictive value of AFMSS and FMSS EE ratings separately from subcomponent ratings. We also ran separate regression models for the prediction of internalising and externalising behaviours. We considered including baseline levels of each outcome as covariates in the relevant model (i.e. baseline internalising as a covariate in prediction of follow-up internalising score). However, this was contraindicated in light of correlations ⩾0.73 signalling strong associations/stability of these scores over time and little remaining variance to be potentially explained by other predictors. There is no community involvement in this study.

Results

Table 2 presents data showing the association between over-arching AFMSS and FMSS EE codes. Statistical tests show evidence of significant association – γ = 0.535 (95% confidence interval (CI) = 0.137 to 0.933); Kendalls’ τb = 0.266, z = 1.994, p = 0.046 – but only 41% raw agreement. Most parents were coded Borderline according to the FMSS, whereas most were coded Low on the AFMSS. Furthermore, a greater proportion of parents were coded as High on the FMSS than on the AFMSS.

Table 2.

Correspondence of over-arching FMSS and AFMSS EE.

AFMSS EE	FMSS EE
AFMSS EE	Low	Borderline	High	Total
Low	13	19	6	38 (74.5%)
Borderline	1	8	3	12 (23.5%)
High	0	0	1	1 (2.0%)
Total	14 (27.5%)	27 (52.9%)	10 (19.6%)	51

FMSS: Five-Minute Speech Sample (Magaña et al., 1986); AFMSS: Autism-Specific Five-Minute Speech Sample (Benson et al., 2011); EE: Expressed Emotion.

Table 3 presents data generated from overlapping FMSS and AFMSS components. These data help to explain differences in overall FMSS and AFMSS EE. Additional FMSS subcomponents that do not overlap with the AFMSS are detailed below. Key differences underlying EOI coding explain the modest raw agreement on FMSS and AFMSS EOI ratings. AFMSS coding is based on Lack of Objectivity and Self-Sacrificing Behaviour, with only one parent rated on the latter (2.0%) – explaining the only AFMSS Borderline EOI rating. While FMSS includes Self-Sacrificing/Overprotective Behaviours, it also comprises other components. In our sample, Moderate and High FMSS EOI ratings were largely driven by those other components, namely, Excessive Detail and Positive Comments. Excessive Detail was coded for seven parents (13.7%), and 21 parents (41.2%) were coded for ⩾5 Positive Remarks. Just one parent (2.0%) was coded with elevated FMSS EOI due to Self-Sacrificing/Overprotective Behaviours (the same parent rated Moderate AFMSS EOI).

Table 3.

Correspondence of FMSS and AFMSS subcomponents.

Codes	AFMSS	FMSS	Tests of association
Initial Statement
Positive	23 (45.1)	22 (43.1)	γ = −0.855 (95% CI = −1.00 to −0.632)
Neutral	27 (52.9)	29 (56.9)	τb = −0.669, z = −4.78, p < 0.001
Negative	1 (2.00)	0 (0.0)	86.3% Raw Agreement
Warmth
High	15 (29.4)	n/a
Moderate	31 (60.8)	n/a	n/a
Low	5 (9.8)	n/a
Relationship
Positive	18 (35.3)	33 (64.7)	γ = −0.949 (95% CI = −1.00 to −0.869)
Neutral	32 (62.7)	16 (31.4)	τb = −0.713, z = −5.18, p < 0.001
Negative	1 (2.0)	1 (2.0)	84.3% Raw Agreement
EOI
High	0 (0.00)	8 (15.7)
Borderline/Moderate	1 (2.0)	18 (35.3)	49% Raw Agreement
Low	50 (98.0)	25 (49.0)
Dissatisfaction
Present	n/a	24 (47.1)
Absent	n/a	27 (52.9)	n/a
Criticism
High	n/a	2 (3.9)
Borderline	n/a	22 (43.1)	n/a
Low	n/a	27 (52.9)
Positive Comments	4.41 (3.65) 0–16	3.96 (2.31) 0–8	rs = 0.747, p < 0.001
Critical Comments	1.08 (1.97) 0–8	0.06 (0.31) 0–2	rs = 0.305, p = 0.030

FMSS: Five-Minute Speech Sample (Magaña et al., 1986); AFMSS: Autism-Specific Five-Minute Speech Sample (Benson et al., 2011); CI: confidence interval; EOI: emotional over-involvement; SD: standard deviation; n/a: not available.

Data are counts (%) except for Positive and Critical Comments which are M (SD) range.

Statistically significant associations existed between AFMSS and FMSS Initial Statement and Relationship codes, including high-level raw agreement. Counts of Positive Comments were consistent at group mean level, across coding systems (the Wilcoxon signed-ranks test (df = 50) = 370, p = 0.222, d = 0.17), with individuals’ scores strongly associated (see Table 3). There was a significant group mean-level difference in Critical Comments, with more of these coded according to the AFMSS than the FMSS (the Wilcoxon test (df = 50) = 276, p < 0.001, d = 0.54), although individuals’ Critical Comments scores were only moderately associated.

Preliminary analysis of putative predictors of child behaviour

Table 4 presents the measures of association between CBCL data and potential covariates. CBCL data showed variability at both baseline (see Table 1) and follow-up (Internalising: M = 16.2, SD = 8.0, range = 2–40; Externalising: M = 17.8, SD = 9.3, range = 0–38). Missing CBCL data presented at baseline (n = 4) and follow-up (n = 6), so to avoid listwise deletion of cases in analyses, we imputed missing values (1) via regression based on scores from the available timepoint or (2) mean substitution (in two cases where both timepoints had missing data) (see Parent, 2012 for justification). As this imputation procedure made no substantive difference to the pattern of results, we report results for the full sample (n = 51).

Table 4.

Potential covariates and child internalising and externalising behaviours.

	CBCL internalising		CBCL externalising
	Baseline	Follow-up	Baseline	Follow-up
Associations
Child
Sex	t(49) = −0.17	t(49) = 0.14	t(49) = −0.66	t(49) = 0.17
ADOS CSS	r = 0.31*	r = 0.10	r = 0.24	r = 0.01
MSEL ELC	rs = −0.33*	rs = −0.32*	rs = −0.14	rs = −0.05
Baseline CBCL Score	n/a	r = 0.73**	n/a	r = 0.75**
Parent/Family
DASS	r = 0.24	r = 0.38*	r = 0.28*	r = 0.30*
Maternal Education	t(49) = 4.38**	t(49) = 3.00*	t(49) = 3.22*	t(49) = 2.29*
Low Income	t(46) = −0.10	t(46) = 1.33	t(46) = −0.24	t(46) = −1.23

CBCL: Child Behavior Checklist (Achenbach & Edelbrock, 1991); ADOS: Autism Diagnostic Observation Scale–Second Edition (Lord et al., 2012); CSS: Calibrated Severity Score; MSEL: Mullen Scales of Early Learning (Mullen, 1995); ELC: Early Learning Composite; DASS: Depression, Anxiety and Stress Scales (Lovibond & Lovibond, 1995); n/a: not available.

p < 0.05; **p < 0.001.

Several significant associations presented between CBCL scores and other measures, including parental DASS scores, maternal education level and child MSEL developmental scores, and – as outlined above – between baseline CBCL scores and their respective outcomes. Variables that were significantly associated with baseline or follow-up CBCL measures were included as controls in respective regression models, entered in the first step.

Table 5 presents the association data between baseline AFMSS and FMSS codes with baseline and follow-up CBCL internalising and externalising behaviour scores. Over-arching AFMSS EE ratings were significantly associated with all measures: CBCL internalising scores at baseline (High/Borderline EE M = 22.90, SD = 6.93; Low EE M = 15.72, SD = 7.56) and follow-up (High/Borderline EE M = 20.50, SD = 7.04; Low EE M = 14.75, SD = 7.81) and CBCL externalising scores at baseline (High/Borderline EE M = 24.95, SD = 8.15; Low EE M = 18.41, SD = 8.71) and follow-up (High/Borderline EE M = 23.78, SD = 7.12; Low EE M = 15.78, SD = 9.16). By contrast, over-arching FMSS EE ratings were not associated with any CBCL measure.

Table 5.

Associations between AFMSS and FMSS codes and baseline and follow-up child CBCL internalising and externalising scores.

		Internalising		Externalising
		Baseline	Follow-up	Baseline	Follow-up
AFMSS	EE (low vs other)	t(49) = 2.96, p = 0.005	t(49) = 2.35, p = 0.023	t(49) = 2.37, p = 0.022	t(49) = 2.86, p = 0.006
	Initial Statement (positive vs other)	t(49) = 0.53, p = 0.599	t(49) = 0.48, p = 0.635	t(49) = 0.99, p = 0.326	t(49) = 2.46, p = 0.017
	Warmth (three categories)	F(2, 48) = 3.97, p = 0.025	F(2, 48) = 2.29, p = 0.112	F(2, 48) = 2.072, p = 0.137	F(2, 48) = 4.79, p = 0.013
	Relationship (positive vs other)	t(49) = 0.97, p = 0.338	t(49) = 0.97, p = 0.337	t(49) = 0.43, p = 0.669	t(49) = 2.28, p = 0.027
	Positive Comments	rs = −0.24, p = 0.108	rs = −0.20, p = 0.200	rs = −0.01, p = 0.971	rs = −0.09, p = 0.549
	Critical Comments	rs = 0.27, p = 0.070	rs = 0.11, p = 0.478	rs = 0.39, p = 0.007	rs = 0.37, p = 0.013
FMSS	EE (three categories)	F(2, 48) = 0.82, p = 0.445	F(2, 48) = 2.75, p = 0.074	F(2, 48) = 2.07, p = 0.137	F(2, 48) = 2.06, p = 0.138
	EOI (three categories)	F(2, 48) = 0.249, p = 0.781	F(2, 48) = 2.24, p = 0.118	F(2, 48) = 2.00, p = 0.147	F(2, 48) = 1.25, p = 0.296
	Criticism (low vs other)	t(49) = 1.61, p = 0.113	t(49) = 0.01, p = 0.990	t(49) = 1.38, p = 0.175	t(49) = 0.53, p = 0.600
	Initial Statement (positive vs neutral)	t(49) = 1.08, p = 0.284	t(49) = 0.60, p = 0.552	t(49) = 1.29, p = 0.205	t(49) = 2.86, p = 0.006
	Relationship (positive vs other)	t(49) = 0.38, p = 0.710	t(49) = 0.27, p = 0.787	t(49) = 0.28, p = 0.782	t(49) = 1.44, p = 0.156
	Positive Comments	r = −0.17, p = 0.261	r = −0.12, p = 0.443	r = 0.09, p = 0.540	r = 0.04, p = 0.805
	Critical Comments	rs = 0.21, p = 0.155	rs = −0.04, p = 0.791	rs = 0.29, p = 0.051	rs = −0.04, p = 0.791

AFMSS: Autism-Specific Five-Minute Speech Sample (Benson et al., 2011); FMSS: Five-Minute Speech Sample (Magaña et al., 1986); CBCL: Child Behavior Checklist (Achenbach & Edelbrock, 1991); EE: Expressed Emotion; EOI: emotional over-involvement.

Regarding subcomponents, AFMSS Initial Statement was associated with follow-up CBCL externalising (Positive Initial Statement M = 14.44, SD = 8.01; Neutral/Negative Initial Statement M = 20.59, SD = 9.51). AFMSS Warmth was associated with baseline CBCL internalising (Low Warmth M = 24.60, SD = 6.65; Moderate M = 18.21, SD = 8.36; High M = 13.84, SD = 6.31) and follow-up CBCL externalising score (Low Warmth M = 25.72, SD = 9.34; Moderate Warmth M = 18.95, SD = 9.25; High Warmth M = 12.85, SD = 7.05). AFMSS Relationship was also associated with follow-up CBCL externalising scores (Positive Relationship M = 13.95, SD = 8.41; Neutral/Negative Relationship M = 19.93, SD = 9.21). Finally, AFMSS Critical Comments were significantly correlated with CBCL externalising behaviour scores at baseline and follow-up. To note, the association between AFMSS EOI and children’s internalising and externalising behaviours could not be addressed due to the restricted range of EOI ratings. In contrast to the AFMSS, only one significant association presented for FMSS key codes: for FMSS Initial Statement with follow-up CBCL externalising behaviour score (Positive Initial Statement M = 22.90, SD = 6.93; Neutral Initial Statement M = 15.72, SD = 7.56).

Predicting child behaviour scores from AFMSS and FMSS EE

While we had planned to include both AFMSS EE and FMSS EE in regressions on baseline and follow-up CBCL internalising and CBCL externalising scores, the lack of associations apparent for FMSS EE with any CBCL measure left only AFMSS EE for inclusion within these models.

Table 6 presents the results for the regression models predicting baseline and follow-up CBCL internalising and externalising scores from AFMSS EE. Regarding internalising scores, the model for baseline CBCL internalising included three covariates, all entered at step one, followed by the dichotomised AFMSS EE (Low vs Moderate/High) entered at step two. Maternal education and child ADOS CSS carried significant unique predictive value for baseline CBCL internalising behaviour at step one, following which the addition of AFMSS EE accounted for further significant predictive value. That is, Moderate/High EE ratings predicted increased baseline CBCL internalising scores. In the model predicting follow-up CBCL internalising behaviour, parental DASS and child MSEL Early Learning Composite (ELC) accounted for significant unique predictive value when entered at step one as covariates, following which the addition of AFMSS EE showed a trend towards added predictive value.

Table 6.

Regression models predicting baseline and follow-up CBCL internalising and externalising scores from AFMSS EE.

CBCL baseline scores							CBCL follow-up scores
	β	p	R	ΔR²	F	p		β	p	R	ΔR²	F	p
Internalising							Internalising
Step 1			0.57	–	7.42	<0.001	Step 1			0.55	–	6.65	<0.001
Maternal Education	−0.40	0.002					Maternal Education	−0.23	0.075
MSEL ELC	−0.19	0.136					DASS Total Score	0.34	0.009
ADOS CSS	0.28	0.033					MSEL ELC	−0.28	0.029
Step 2			0.66	0.11	8.63	0.005	Step 2			0.60	0.06	3.99	0.052
AFMSS EE	0.33	0.005					AFMSS EE	0.24	0.052

Externalising							Externalising
Step 1			0.38	–	3.99	0.025	Step 1			0.34	–	3.22	0.049
Maternal Education	−0.26	0.062					Maternal Education	−0.18	0.198
DASS Total Score	0.22	0.119					DASS Total Score	0.25	0.075
Step 2			0.47	0.08	4.68	0.036	Step 2			0.48	0.12	7.06	0.011
AFMSS EE	0.28	0.036					AFMSS EE	0.34	0.011

CBCL: Child Behavior Checklist (Achenbach & Edelbrock, 1991); AFMSS: Autism-Specific Five-Minute Speech Sample (Benson et al., 2011); EE: Expressed Emotion; MSEL: Mullen Scales of Early Learning (Mullen, 1995); ELC: Early Learning Composite (Standard Score; population M = 100, SD = 15); SD: standard deviation; DASS: Depression Anxiety Stress Scale (Lovibond & Lovibond, 1995); ADOS: Autism Diagnostic Observation Schedule (Lord et al., 2012); CSS: Calibrated Severity Score (range = 1–10).

Regarding externalising CBCL scores, while maternal education and DASS were jointly predictive of baseline CBCL externalising scores at step one (albeit with neither carrying significant unique predictive value), the inclusion of AFMSS EE at step two made a significant unique predictive contribution. A similar pattern of results was observed in the model predicting follow-up CBCL externalising scores, whereby maternal education and DASS were jointly predictive (but with neither carrying unique predictive value) as covariates, following which AFMSS EE carried significant additional predictive value. In both instances, Moderate/High EE ratings predicted increased baseline and follow-up CBCL externalising scores.

Predicting child behaviour scores from AFMSS and FMSS subcomponents

Next, we repeated hierarchical regressions for CBCL internalising and externalising behaviour scores from subcomponent codes (Warmth, Relationship, etc.). Again, the plan was to include both AFMSS and FMSS subcomponents. However, only one association was observed between FMSS subcomponents (i.e. Initial Statement) and follow-up CBCL externalising behaviour. Given the significant association between Initial Statement across the FMSS and AFMSS (see Table 3), a separate regression analysis was not conducted as the pattern of results was considered likely to be similar.

The results for regression models predicting baseline CBCL internalising and externalising scores from the AFMSS subcomponents are shown in Table 7. For baseline CBCL internalising scores, at step one, maternal education and child ADOS CSS both made a significant unique predictive contribution for baseline CBCL internalising, following which the addition of AFMSS Moderate (vs High) Warmth – but not AFMSS Low (vs High) Warmth – accounted for further significant variance. Compared to High Warmth, Moderate Warmth was associated with lower child internalising behaviours. No regression analysis was run for follow-up CBCL internalising behaviour, due to the lack of any observed predictors significantly associated with this particular outcome.

Table 7.

Regression models predicting baseline and follow-up CBCL internalising and externalising behaviours from AFMSS subcomponents.

CBCL baseline scores							CBCL follow-up scores
	β	p	R	ΔR²	F	p		β	p	R	ΔR²	F	p
Internalising							Externalising
Step 1			0.57	–	7.42	<0.001	Step 1			0.34	–	3.22	0.049
Maternal Education	−0.40	0.002					Maternal Education	−0.18	0.198
MSEL ELC	−0.19	0.136					DASS Total Score	0.25	0.075
ADOS CSS	0.28	0.033					Step 2			0.71	0.38	6.51	<0.001
Step 2			0.67	0.12	5.02	0.011	Pos. Initial Statement^b	−0.42	0.002
Low Warmth^a	0.00	0.980					Low Warmth^a	0.30	0.027
Mod. Warmth^a	−0.35	0.022					Mod. Warmth^a	0.19	0.149
							Pos. Relationship^b	−0.09	0.471
Externalising							Critical Comments	0.11	0.370
Step 1			0.38	–	3.99	0.025
Maternal Education	−0.26	0.062
DASS Total Score	0.22	0.119
Step 2			0.48	0.09	5.61	0.022
Critical Comments	0.31	0.022

CBCL: Child Behavior Checklist (Achenbach & Edelbrock, 1991); AFMSS: Autism-Specific Five-Minute Speech Sample (Benson et al., 2011); MSEL: Mullen Scales of Early Learning (Mullen, 1995); ELC: Early Learning Composite (Standard Score; population M = 100, SD = 15); SD: standard deviation; DASS: Depression Anxiety Stress Scale (Lovibond & Lovibond, 1995); ADOS: Autism Diagnostic Observation Schedule (Lord et al., 2012); CSS: Calibrated Severity Score (range = 1–10); Mod.: moderate; Pos.: positive.

Versus high.

Versus other.

Regarding baseline CBCL externalising scores, again, while maternal education and DASS scores were jointly predictive at step one, neither carried significant unique predictive value for baseline scores. Nevertheless, the subsequent addition of AFMSS Critical Comments contributed significant additional predictive value, whereby more comments predicted higher baseline CBCL externalising scores. In the prediction of follow-up CBCL externalising scores, maternal education and DASS showed combined significant predictive value. At step two, AFMSS Initial Statement (Positive vs Neutral/Negative) provided further unique predictive value, with Positive Initial Statements predicting lower externalising problem scores. In addition, Low (vs High) Warmth carried significant predictive value, whereby Low Warmth predicted greater externalising behaviours compared to High Warmth. There was no significant additional contribution of Warmth (Moderate vs High), AFMSS Relationship and Critical Comments.

Discussion

The first aim of this study was to directly compare AFMSS and FMSS ratings generated from a single sample of parents of autistic preschoolers. Our first hypothesis was largely supported in that analogous FMSS and AFMSS codes were associated with one another, including AFMSS and FMSS EE but also subcomponents such as Initial Statement, Relationship and Positive Comments. The exception here was Critical Comments, which were coded significantly more in the AFMSS. We had hypothesised that EOI ratings would show poorer alignment across FMSS and AFMSS schemes. Our data did not allow us to formally test this hypothesis, but we observed substantial variability in FMSS EOI ratings, compared to striking homogeneity in AFMSS EOI where only one parent was not rated Low. Hence, we conclude that EOI is quantifiably different across coding schemes. Our second hypothesis – that child behaviour problems might be better predicted by AFMSS than FMSS ratings – was also supported. That is, whereas AFMSS measures predicted both concurrent and subsequent child internalising and (even more so) externalising problem behaviours, FMSS ratings were largely unrelated to child behaviour.

Cross-scheme comparison

Innovativeness of this research is that it is the first to directly compare AFMSS and FMSS codes in the same cohort. Broadly, we found AFMSS and FMSS EE to be statistically associated, suggesting these schemes are tapping a similar construct. Yet, raw agreement across overall EE ratings was low (41%). A contributory factor was the homogeneous AFMSS EOI ratings, reducing variability in over-arching AFMSS EE, compared to greater variability in FMSS EOI and EE ratings. That is, the variability (or lack thereof) observed here can be explained by differences in how EOI in particular is rated across the two schemes.

AFMSS EOI is rated from two subcomponents – Self-Sacrificing/Overprotective Behaviours and Lack of Objectivity – with only Self-Sacrificing/Overprotective Behaviours rated (on just one occasion). The FMSS EOI includes Self-Sacrificing/Overprotective Behaviour, yet our elevated FMSS EOI ratings were largely driven by Excessive Positive Comments and Excessive Detail – neither of which are coded in the AFMSS. When compared to the FMSS, the relative homogeneity of AFMSS EOI and EE ratings was therefore unsurprising.

The AFMSS coding scheme – in particular, excluding Positive Remarks about the child from ratings of EOI – feasibly provides a more conservative (and probably more appropriate) measure of EOI and EE, than does the FMSS in cohorts such as ours where parents are reporting on preschool-aged autistic children. This is plausible given both the high likelihood of parental positive comments concerning young children (Daley et al., 2003; Wamboldt et al., 2000) and that positive comments are considered in fact to be supportive of young children’s emotional and behavioural development (Psychogiou et al., 2017).

Benson et al. (2011) also reported low EOI among the vast majority (97%) of parents of preadolescent-aged autistic children, and no instances of high EOI (the remaining 3% received moderate EOI ratings). The age of children, for whom parents are basing their narratives on, thus does not appear to influence the degree of variability in AFMSS EOI ratings. Rather, the truncated nature of EOI ratings across multiple samples, and lack of association between parental EOI and children’s behavioural difficulties, brings to question the relevance and validity of the construct among parents of children on the autism spectrum. Alternatively, it may be that the EOI is a valid construct with implications for children’s internalising and externalising difficulties but is not sensitively captured by the current coding schemes. Further research is needed to disentangle these possibilities.

EE and child behaviour

Past research has reported parental EE to be more strongly associated with externalising than internalising child behaviour (Bolton et al., 2003; McCarty et al., 2004; Peris & Baker, 2000). Here, we also found robust evidence of parental EE being predictive of concurrent and subsequent externalising behaviour in preschoolers. This is in contrast with Benson et al. (2011) who reported no such association in their sample of parents of preadolescent autistic children rated with the AFMSS, although their composite behaviour measure may have contributed to the null findings since parental EE appears to be differentially associated with different child behaviour problems.

Yet, we also found AFMSS EE to predict concurrent internalising behaviour, and approaching significance as a predictor of subsequent internalising behaviour. While previous research with children on the autism spectrum (Baker et al., 2019) and from non-clinical samples (Gravener et al., 2012; Han & Shaffer, 2014) has noted associations between parental EE and child internalising behaviours, this has largely been in the context of the explanatory value of criticism rather than of over-arching EE scores – a pattern not replicated in this study. Hence, further exploration of patterns of predictors of internalising behaviours in the context of newly diagnosed children is warranted.

AFMSS subcomponents

Of interest were our observed associations between children’s externalising behaviour and AFMSS Critical Comments (at baseline) and Warmth (at follow-up). Our findings are consistent with Baker et al. (2019) who reported that AFMSS Critical Comments predicted concurrent child externalising behaviours. The relationship observed between Warmth and later child externalising behaviours also appears to mirror Hickey et al.’s (2020) findings where maternal Warmth – coded from the FMSS according to conventions in the original CFI (Brown & Rutter, 1966; Vaughan & Leff, 1976) – predicted later child behaviour.

A novel finding was the negative association observed between Warmth and concurrent child internalising behaviours, whereby Moderate (vs High) Warmth predicted fewer internalising problems. This effect has not been reported in the extant literature. However, Baker et al. (2019) found an interaction between Warmth and Critical Comments in the prediction of child internalising behaviours, whereby criticism was associated with increased internalising behaviours in the context of Moderate, but not High, Warmth. Relatively small sample size precluded us from including interaction terms in our regression models. We consider High Warmth may plausibly indicate some over-protectiveness here, thereby explaining the association with child internalising/anxiety behaviours.

A further novel finding was that children had lower externalising scores when the first remark made by their parent was Positive (vs Neutral/Negative). While we did find over-arching AFMSS EE predictive of concurrent and subsequent child behaviour here, discussion continues around the relative usefulness of this broad construct in the context of families with autistic children (Baker et al., 2011, 2019) with recent studies tending to focus instead on exploring subcomponents, especially Warmth and Criticism (Baker et al., 2019; Hickey et al., 2020; Psychogiou et al., 2017). Our observed associations between child behaviour and AFMSS Critical Comments, Warmth and Initial Statement suggest that there may indeed be value in further investigating subcomponents as unique/combined predictors of child behaviour. Furthermore, since our findings appear to be driven by the more positive end of the EE continuum, there is an indication that the AFMSS captures EE differently – when compared to the CFI or FMSS – in this population.

Limitations and future directions

Given the strong observed stability of CBCL scores – for both internalising and externalising behaviours – we opted not to include baseline scores as covariates within respective regression models. We were most interested in the relatively utility of the AFMSS versus FMSS in predicting child behaviour. Future research within a larger, longitudinal sample is required to permit exploration of the AFMSS as a predictor of child behaviour trajectories. In addition, we acknowledge our modest sample size precludes strong conclusions to be drawn and highlight the preliminary nature of this research. We further acknowledge our multiple analyses increase the likelihood of a Type-1; however, this was balanced with the likelihood of encountering Type-2 errors due to our limited sample size. There is likewise a possibility of common-rater bias as both the FMSS/AFMSS and measures of child psychopathology were based on perceptions of the same parent.

Since criticism was rare across both coding schemes, future research should attempt to sample families of young children across a greater range of criticism to reach definitive conclusions about utility of EE measures with this population. Echoing earlier research (Baker et al., 2019; Benson et al., 2011; Maughan & Weiss, 2017), low/negative ratings were rare across our AFMSS sample. Modifying AFMSS scoring to increase coding heterogeneity may be valuable. Finally, exploring how FMSS/AFMSS codes relate to other previously associated child and parent characteristics needs further investigation in the context of childhood autism (Benson et al., 2011; Romero-Gonzalez et al., 2018).

Conclusion

These preliminary data strengthen the position that the AFMSS may better capture EE, than the FMSS, among parents of autistic preschool-aged children.

Footnotes

Acknowledgements

The authors thank the families for their participation in this study. The authors also thank Zoe Lazaridis, Daniel Berends, Megan Clark, Megan Grant, Katherine Natoli, Erin O’Connor, Rachael Rankin, Veronica Rose and the Victorian ASELCC staff who supported with data collection, technical support and/or recruitment.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This project work was funded by the La Trobe University’s Social Research Assistance Platform Grant and involved data collected as part of a larger study funded by the Australian Government Department of Social Services.

ORCID iDs

Jodie Smith

Catherine A Bent

Lacey Chetcuti

Kristelle Hudry

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