Evaluation of a recovery-oriented care training program for mental healthcare professionals: Effects on mental health consumer outcomes

Abstract

Objectives:

To examine the effects of a recovery-oriented care training program for mental healthcare professionals on mental health consumer outcomes.

Methods:

The Mental Health Recovery Measure (MHRM) and the Recovery-Promoting Relationship Scale (RPRS) were administered to a sample of 142 consumers with severe mental illness. A repeated measurement design with six measurement occasions was used.

Analyses:

Separate analyses were performed for the MHRM and RPRS subscales. Data were analyzed by means of the software package AMOS for structural equation modeling. First, the means of the five scales were computed at each measurement occasion. Next, two series of regression analyses were conducted: the first series aimed to ascertain whether gender and age have a significant effect on the MHRM and RPRS scores, and the second series aimed to detect a systematic trend in the average scale response of the MHRM and RPRS.

Results:

Scores showed a significant change over time for the subscale ‘Learning & new potentials’ of the MHRM. Significant effects were also found for gender, with men scoring higher than women on the subscales ‘Self-empowerment’ and ‘Learning & new potentials’. Age had no effect on the MHRM and RPRS. The scores on the RPRS showed no significant change over time.

Conclusions:

One year after completion of the recovery-oriented training program for professionals, positive results were found for two subscales of the MHRM, that is, ‘Self- empowerment’ and ‘Learning & new potentials’.

Keywords

Mental health recovery recovery and gender recovery-promoting relationship

Introduction

During the last decade, recovery has become a more mature concept in Anglo-Saxon countries (Slade, Williams, Bird, Leamy, & Le Boutillier, 2012). The National Consensus Statement on Mental Health Recovery defined recovery as ‘a journey of healing and transformation enabling a person with a mental health problem to live a meaningful life in a community of his or her choice while striving to achieve his or her full potentials’ (Substance Abuse and Mental Health Service Administration (SAMSHA), 2004; South London and Maudsley NHS Foundation Trust and South West London and St. George’s Mental Health NHS Trust (SLAM/SWLSTG), 2005). Recovery is focused on personal growth, hope and autonomy (Meehan, King, Beavis, & Robinson, 2008), as well as on learning to live with the negative consequences of the disease (Buckley-Walker, Crowe, & Caputi, 2010). This vision of recovery is based on the patient’s perspective (Jacobson & Greenley, 2001; Young & Ensing, 1999) and is seen as a continuing process of change which is not illness focused (Anthony, 2004). Recent studies show that recovery is linked with terms as connectedness, hope and optimism about the future, identity, meaning in life and empowerment (Leamy, Bird, Le Boutillier, Williams, & Slade, 2011; J. A. W. M. Van Gestel-Timmermans, Brouwers, & Van Nieuwenhuizen, 2010). Todd, Jones, and Lobban (2012) found that recovery is, indeed, not simply the absence of symptoms but involves personal responsibility and empowerment, and also being connected with other people. In this way, recovery differs from the traditional medically oriented approach of recovery.

Discussion continues regarding what professionals can do in daily practice to support the unique process of recovery. Harrow and Jobe (2007; 2010) stated that, even without treatment, 50% of the patients who are diagnosed with a psychotic disorder experience some periods of recovery over a number of years. However, it is also known that mental healthcare professionals can affect the recovery process in several ways, that is, they can support and facilitate (Slade, 2009) as well as hinder the difficult path of recovery (Onken, Dumont, Ridgeway, Dornan, & Ralph, 2006). There is increasing evidence that ‘inspiring hope’ and having the ability to ‘empower the patient’ are crucial professional competencies to support or facilitate the recovery process (Lakeman, 2010; Le Boutillier et al., 2011; Schrank, Bird, Rudnick, & Slade, 2012; J. Van Gestel-Timmermans, Van den Bogaard, Brouwers, Herth, & Van Nieuwenhuizen, 2010).

Considerable research has focused on recovery from serious mental disorders (Emsley, Chiliza, Asmal, & Lehloenya, 2011; Harrow & Jobe, 2010). Global characteristics of patients (such as temperament, personality and cognitive traits) seem to influence the course of severe mental illnesses (Harrow & Jobe, 2010) and, therefore, the recovery process. Long-term outcomes appear to be related to gender. For example, differences between men and women were found in the onset and course of disorders, relapse rates and social functioning (Grossman, Harrow, Rosen, Faull, & Strauss, 2006; Ochoa, Usall, Gobo, Labad, & Kulkarni, 2012; Sajatovic, Jenkins, Strauss, Butt, & Carpenter, 2005) as well as the duration of the untreated psychosis (Cascio, Cella, Preti, Meneghelli, & Cocchi, 2012; Davidson, Chinman, Sells, & Rowe, 2006).

Besides the influence of a patient’s personal characteristics, the existence of meaningful relationships is receiving increasing attention with regard to recovery from severe mental illness (Redko, Rapp, Elms, Snijdes, & Carlson, 2007). These relationships can be with peers, family members, meaningful others and/or professionals (Hobbs & Baker, 2012; Schön, Denhov, & Topor, 2009; Schön, 2010). According to the recovery movement, the relationship with the professional has to be based on the following characteristics: empathy, presence, disclosure, equality and reciprocity (Boevink et al., 2009; Davis & Lysaker, 2007; Farkas & Anthony, 2010; Wilken & Den Hollander, 2005). The existing paternalistic, illness-oriented approach needs to change to a more recovery-oriented, collaborative, autonomy-stimulating approach (Sowers, 2005), and the services provided need to focus on the belief that mental healthcare users and providers are partners in treatment. The mental healthcare service should be offered within a context of a collaborative relationship with clients (Borg & Kristiansen, 2004; Green et al., 2008; Leamy et al., 2011; Slade, 2009; Russinova, Rogers, & Ellison, 2006).

All these recovery-oriented care characteristics require a different attitude toward recovery from the mental healthcare professional. Therefore, professionals need to be trained in the recovery approach.

Several recovery-oriented training programs for professionals have been evaluated and showed that the attitude and knowledge of mental healthcare professionals toward recovery can change after their training (Bedregal, O’Connell, & Davidson, 2006; Crowe, Deane, Oades, Caputi, & Morland, 2006; Green et al., 2008; Kymalainen et al., 2010; Tsai, Salyers, & Lobb, 2010; Tsai, Salyers, & McGuire, 2011; Wilrycx, Croon, Van den Broek, & Van Nieuwenhuizen, 2012). However, little is known about the effect of these changes in attitude and knowledge of professionals on mental healthcare consumer outcomes.

Therefore, this study evaluates the effectiveness of a recovery-oriented training program for mental healthcare professionals. More specifically, the study aimed to answer the following questions:

Does a recovery-oriented training program for professionals promote patients’ experienced empowerment and autonomy?

To what extent are empowerment and the perceived working relationship related to patients’ characteristics such as gender and age?

Do patients perceive the relationship with the professional to be more recovery-oriented after the professional has completed the training program?

Methods

Design

In this study (which took 2 years to complete), a repeated measurement design with six measurement occasions was employed.

Measures

Recovery-Promoting Relationship Scale (RPRS)

The RPRS is a self-report questionnaire for patients. The Dutch RPRS is a 22-item scale that measures the generic components of mental health providers’ recovery-promoting professional competence (Russinova et al., 2006; Wilrycx, Croon, Van den Broek, & Van Nieuwenhuizen, 2011). Items are scored on 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree), and also with 5 (not applicable).

The Dutch version of the RPRS consists of two subscales. The first comprises 17 items and reflects the recovery-related strategies, for example, providing ‘hope’ and the ability to ‘empower the patient’. The second subscale comprises five items and represents the provider’s skills to enhance client’s self-acceptance. The Cronbach’s alphas for the subscales are .93 and .87, respectively. The correlation between the mean scale scores for both scales was .66.

Mental Health Recovery Measure (MHRM)

The MHRM (Van Nieuwenhuizen, Wilrycx, Moradi, & Brouwers, 2013; Young & Bullock, 2003) is a self-report instrument designed to assess the recovery process of persons with severe mental illness. The Dutch 30-item version comprises three subscales: Self-empowerment, Learning & new potentials and Spirituality. All items are rated on a 5-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’. The Cronbach’s alpha coefficients for the three subscales are .90, .86 and .94.

Procedure

All mental healthcare workers of the department ‘Impact’ (the department for long-term mentally ill people in Breda/Etten-Leur) were asked to participate in a training program on recovery-oriented care. All staff participants were verbally informed by their managers and also received an information flyer about the program; all gave informed consent before the study started. The recovery-oriented training program was mandatory for all professionals. Parallel to the training program, an evaluation study was conducted to assess the effects of the recovery-oriented training program on professionals’ and patients’ outcomes. The study started at the beginning of 2008 and ended in the summer of 2010 (Figure 1). Prior to participation, all patients were verbally informed by their personal mental-health professional, received written information about the research program and all provided informed consent.

Figure 1.

Flow chart of the study assessments.

In this study, patient participants were asked to fill in the MHRM and the RPRS. Patient participants received the questionnaires by regular mail; they were asked to complete the questionnaires as soon as possible, but within at least 2 weeks after receiving the questionnaires.

Prior to study start, the regional Medical Ethics Approval Committee for Mental Health Care Institutions (METIGG) was approached. In the Netherlands, according to the Medical Research Involving Human Subjects Act, ethical approval was not required for this study.

Recovery-oriented training program

To implement the new recovery vision, a recovery-oriented care training program was developed by three major mental healthcare organizations: that is, two rehabilitation organizations Rehabilitation ‘92, (Korevaar & Dröes, 2011) and STORM rehabilitation (Wilken & Den Hollander, 2005) and one peer-support organization (HEE, Boevink, 2006), The ‘Recovery and recovery-oriented care’ training program was specifically developed for the mental healthcare network ‘Impact’ for long-term mentally ill people. The main goal of the project was to create and promote a new culture toward recovery from serious mental illness: how can treatment promote the recovery process of patients with long-term psychiatric problems and does the relationship with the mental health professional facilitate recovery? (Anthony, 2000; Boevink & Dröes, 2005; Hugo, 2001; New Zealand Mental Health Commission, 2001).

The recovery-oriented care training program was given in two separate intensive training sessions, in other words, two experimental conditions, one in 2008 and a second one in 2009. The training program was developed for all professionals (e.g. psychologists, psychiatrists, secretaries, managers and nurses) who are in close contact with mental healthcare patients. The recovery-oriented training program consisted of two seminars given in a 2-day session every 6 months; participants were randomly selected and 20 groups were formed with 10–16 professionals per group. The first seminar ‘Basics of recovery and recovery-oriented care’ (experimental condition A/intervention A) was given in the first half of 2008. The second seminar (experimental condition B/intervention B) was given in spring and summer of 2009; this seminar focused on attitudes toward recovery and the way the professional is able to stimulate and facilitate recovery within the client. Figure 1 presents an overview of the training seminars with the different measurement occasions (for professionals and patients) and the corresponding response rates. Both training seminars were given in close cooperation with an expert from the peer-support organization.

More detailed information on the training program is available in Wilrycx et al. (2012).

Data analysis

Separate analyses were performed for the three MHRMs and the two RPRSs. First, the means of the five scales were computed at each measurement occasion. Next, two series of regression analyses were conducted: the first aimed to ascertain whether gender and age have a significant effect on the MHRM and RPRS scores (= response variables), whereas the second series aimed at detecting a systematic trend in the average scale response of the MHRM and RPRS. The series of regression analyses was based on thefollowing statistical model for the scores of individual i at time t

y_{i t} = μ_{t} + α D g e n d e r + β_{1} D a g e_{1} + β_{2} D a g e_{2} + μ_{i} + e_{i t}

Separate analyses were performed for the three MHRMs and the two RPRSs. In the model equation above, these scale scores are represented by $y_{i t}$ for individual i attime t.

Since gender and age were assumed to have a potential effect on the scale scores, both variables were included in the regression equation by means of dummy variables. The dummy variable Dgender represents gender and was defined as Dgender = 0 for Women, Dgender = 1 for Men. A subject’s age was categorized in three categories: 18–42, 43–57 and 57–78 years, and these three categories were represented by two dummy variables Dage₁ and Dage₂ with the third category being taken as the reference category. The intercepts µ_t represent the overall mean at time t after controlling for gender and age. It is expected that these means will increase over time, indicating that the intervention has a cumulative positive effect on patients’ outcomes. Finally, the quantities µ_i are random effects representing individual differences between the patients that remain constant over time and are not explained by gender and age. These random effects, which are assumed to be uncorrelated with both gender and age, are introduced in the model to account for the eventual dependencies among the observations made on the same subject.

The model described above could in principle be estimated and tested as a multilevel regression model by the appropriate SPSS procedure MIXED. However, this procedure applies list-wise deletion to resolve the problem of missing data. As is commonly observed in longitudinal studies, the dropout of subjects increases over time and this study is no exception. Starting with a sample of 142 subjects, dropout was cumulative so that, at the end of the study, about 30% of the subjects were no longer participating (Figure 1). List-wise deletion would then result in a considerable loss of subjects, although most had provided useful information at the start of the study before finally dropping out. Therefore, it was decided to analyze the data by means of the software package AMOS for structural equation modeling. Application of this package results in full information maximum likelihood (FIML) estimates of the model parameters. In this approach, every subject with at least one observed score remains in the analysis, and all their observed scores effectively contribute to the estimation procedure. It has been shown that FIML has better statistical properties than ad hoc methods like list-wise deletion (Enders, 2001; Newman, 2003). However, this approach requires that the model is recast in the form of a structural equation model. As indicated by Rovine and Molenaar (2000), this can be achieved by considering the individual random effects u_i as scores on a common latent factor on which all measurement occasions have a fixed unit factor loading, and by simultaneously treating the explanatory dummy variables as exogenous in the model.

Results

Sample characteristics

In this study, a total of 360 patients with long-term psychological/psychiatric disorders from Impact were approached (ad random) either personally or by telephone. Only participants aged ≥ 18 years and with a good understanding of the Dutch language were approached. A sample of 142 (i.e. 39% of the approached population) agreed to participate. The remaining 61% either felt unable to participate or had no interest. The average age of the participants was 49.1 years (range 18–78 years; standard deviation (SD) 13.1) and of the non-participants 50.6 years (range 18–93 years; SD 17). The mean number of years of treatment of the participants was 14.16 years (SD 10.3). Table 1 presents the characteristics of the participants.

Table 1.

Characteristics of the mental healthcare consumers: participants (N = 142) and non-participants (N = 218).

	Participants	Non- participants
	M	M
Age in years	49.1 (13.1)	50.6 (17)
Number of years of treatment	14.06 (10.3)	Data not available
	N (%)	N (%)
Gender	89 (63)	101 (46)
Female
Psychiatric characteristics
DSM IV-R classification Axis I
Schizophrenia, psychotic disorders	46 (35)	91 (44)
Mood disorders	40 (31)	59 (29)
Anxiety disorders	8 (6)	15 (7)
Substance-related disorder	7 (5)	8 (2)
No diagnosis on Axis I	4(3)	5 (3)
Other (including ADHD and ASD)	25 (19)	30 (15)
DSM IV-R classification Axis II
Cluster A	4 (3)	14 (7)
Cluster B	20 (16)	29 (14)
Cluster C	17 (14)	21 (10)
NOS	23 (18)	44 (21)
Other	6 (3)	8 (4)
No diagnosis on Axis II	42 (33)	78 (37)

ADHD: Attention Deficit Hyperactive Disorder; ASD: Autism Spectrum Disorder; DSM IV-R: Diagnostic and Statistical Manual of Mental Disorders–Fourth Edition–Revised; NOS: Not Otherwise Specified.

In a preliminary analysis, the 142 patients who participated were compared with the 218 non-participants. The average age of the participants was 49.1 years (range 18–78 years; SD 13.1) and of the non-participants 50.6 years (range 18–93 years; SD 17) (Table 1). There was no significant difference between the two groups with respect to age (t = −0.93, degrees of freedom (df) = 358, p = .35). To compare the two groups for differences on the psychiatric diagnosis (main diagnosis on Axis I and II) and gender, chi-square independence tests were performed. The only significant result was found for gender: c² = 9, 22 (df = 1, p = .002), whereby significantly more women than men agreed to participate. There were no significant differences between the two groups for Axis I (χ² = 7.115, df = 6, p = .31) and Axis II (χ² = 5.620, df = 6, p = .47) diagnoses. Therefore, we can conclude that, except for gender, there were no systematic differences between the participants and non-participants.

As a first step in the proper analysis of the data, the means of the five scales were computed at each measurement occasion (Table 2). An inspection of the scale means for ‘Self-empowerment’ and ‘Learning & new potentials’ shows that the means remain constant over the first five time points, but that the mean at T = 6 is clearly larger than the overall level at the previous five time points.

Table 2.

Scale means at different measurement occasions with standard errors given in parentheses.

Scale	Mean T1 ${\hat{μ}}_{1}$	Mean T2 ${\hat{μ}}_{2}$	Mean T3 ${\hat{μ}}_{3}$	Mean T4 ${\hat{μ}}_{4}$	Mean T5 ${\hat{μ}}_{5}$	Mean T6 ${\hat{μ}}_{6}$
MHRM
Self-empowerment	3.34 (.06)	3.25 (.06)	3.33 (.06)	3.31 (.08)	3.36 (.06)	3.42 (.08)
Learning and new potentials	3.51 (.05)	3.47 (.06)	3.47 (.05)	3.48 (.06)	3.51 (.06)	3.68 (.06)
Spirituality	3.16 (.10)	2.98 (.11)	3.03 (.11)	3.06 (.11)	3.13 (.11)	3.15 (.11)
RPRS
Hopefulness and empowerment	3.60 (.04)	3.49 (.05)	3.54 (.06)	3.56 (.06)	3.61 (.06)	3.42 (.07)
Self-acceptance	3.53 (.05)	3.38 (.07)	3.49 (.06)	3.47 (.07)	3.47 (.07)	3.38 (.11)

MHRM: Mental Health Recovery Measure; RPRS: Recovery-Promoting Relationship Scale.

Next, two series of regression analyses were performed: a first series aimed to ascertain whether gender and age have a significant effect on the response variables, whereas the second series aimed at detecting a systematic trend in the average scale response.

In the first series of analyses, the full regression model described in the previous section was estimated for each of the three MHRMs and two RPRSs, and the significance of the regression coefficients of the three dummy variables was tested. For each of the five scales, the null hypothesis, which states that all three regression coefficients $α, β_{1} a n d β_{2}$ are equal to zero, was tested by means of a conditional likelihood ratio test against the alternative model, which leaves these three parameters free. Under the null hypothesis, the test values follow a chi-square distribution with three degrees of freedom, this being equal to the number of constraints imposed on the parameters. Table 3 summarizes the results of this conditional testing procedure.

Table 3.

Conditional chi-square tests for significance of regression coefficients of dummy variables.

Scale	CMIN (df = 3)	p-value
MHRM
Self-empowerment	11.723	.008
Learning and new potentials	8.845	.031
Spirituality	4.986	.173
RPRS
Hopefulness and empowerment	.277	.964
Self-acceptance	1.572	.666

CMIN= Chi-square value; df: degrees of freedom; MHRM: Mental Health Recovery Measure; RPRS: Recovery-Promoting Relationship Scale.

The hypothesis of no effects for gender and age could only be rejected for the two subscales of the MHRM: Self-empowerment and Learning & new potentials. Furthermore, closer inspection of the results for the individual regression coefficients showed that for both scales, it was the regression coefficient for the dummy variable representing gender that made the difference. The estimates of this regression coefficient were .364 (standard error (SE) = .107, t = 3.391, p < .001) and .201 (SE = .091, t = 2.217, p = .027) for MHRM F1 (self-empowerment) and MHRM F2 (Learning and the actualization of new potentials), respectively. The positive values of the regression estimates indicate that, holding all other explanatory variables constant, men tend to score higher on the two scales than women. On the other hand, the estimates of the dummy variables representing the age categories failed to show a significant value, revealing no systematic differences between the age categories.

A second series of regression analyses aimed at testing whether the mean parameters µ_t do (or do not) change over time, by carrying out conditional chi-square tests of the null hypothesis that all mean parameters are equal against the alternative model which leaves these parameters unconstrained. Table 4 summarizes the results of these conditional chi-square tests. For each scale, it supplies the value of the chi-square test statistics and its probability level under a chi-square distribution with 5 degrees of freedom. The last column contains the estimate of the constant mean scale score estimated under the null hypothesis, together with its standard error. Since gender had a significant effect on the two subscales of the MHRM, the comparison of the scale means for these two subscales was based on a model in which gender was included as an explanatory variable. For the remaining scales, the comparison of the scale means was based on analyses with no additional explanatory variables.

Table 4.

Conditional chi-square test of null hypothesis of equal means at different measurement occasions.

Scale	$χ^{2}$ (df = 5)	p-value	$\overset{\land}{μ}$
MHRM
Self-empowerment	6.382	.27	3.20 (.07)
Learning and new potentials	14.505	.01	3.42 (.06)
Spirituality	5.532	.35	3.10 (.09)
RPRS
Hopefulness and empowerment	6.131	.29	3.56 (.03)
Self-acceptance	7.757	.17	3.47 (.04)

df: degrees of freedom; MHRM: Mental Health Recovery Measure; RPRS: Recovery-Promoting Relationship Scale.

The results for MHRM F1 (Self-empowerment) and MHRM F2 (Learning & new potentials) are based on analyses in which gender is included as an explanatory variable. The analyses for the remaining scales did not include explanatory variables.

The hypothesis of no mean change over time could only be rejected for one scale: the second subscale of the MHRM, that is, Learning & new potentials. Closer inspection of the scale means for this variable shows that the means remain constant over the first five time points, but that the mean at T = 6 is clearly larger than the overall level at the five previous time points. This was confirmed by a significance test of the hypothesis that only the means over the first five time points remained constant, while the mean at T = 6 was left unconstrained. This hypothesis could not be rejected: χ² = 1.914 for df = 4 and p = .752. Including the mean at T = 6 in the equality constraint led to a clear rejection of the corresponding hypothesis, as is shown by the conditional chi-square test statistic χ² = 12.591 for df = 1 and p < .001.

Discussion

This study is a first attempt to examine the indirect effects of a recovery-oriented training program for professionals toward the recovery vision on mental health consumer’s outcomes. The results show that patients make a start with their individual recovery process during and after the recovery-oriented training program of professionals.

The positive changes in mean scores over time on the subscales ‘Self-empowerment’ and ‘Learning & new potentials’ of the MHRM may indicate that professionals could be able to empower patients, and can stimulate patient’s autonomy. The results also show that men have better results on these subscales of the MHRM than women. In this way, gender was associated with higher ratings with a greater increase over time. This suggests that men seem easier to empower than women and more often grasp the opportunity to undertake new activities. Research on severe mental illnesses has shown that gender can indeed influence the recovery process. The following characteristics from a gender perspective might explain our results. First, men tend to fulfill the expected norm more than women (West & Zimmerman, 1987) and, second, independence is more highly valued by men than by women (Sajatovic et al., 2005). Furthermore, the existence of role stereotyping behavior of the professional toward patients can promote the recovery process. Schön (2010) mentioned that the existence of different expectations placed on men and women by society may play an important role in the recovery process and, therefore, in the treatment of people with severe mental illness.

On the other hand, age failed to have a significant effect on any scale. Because younger persons are generally more eager to learn (Francke, Smit, De Veer, & Mistiean, 2008), it was expected that younger patients would score better on (specifically) the subscale of the MHRM (Learning & new potentials) than older patients; however, this did not occur. Because in this study, no difference was found between younger and older patients, this could mean that age is not related to the moment that patients make a start with their recovery process. Nevertheless, the positive results on two subscales of the MHRM support the findings of Harrow and Jobe (2010) that (spontaneous) episodes of recovery can occur even without treatment.

The present results also show that the relationship with the professional is not experienced as a more recovery-oriented one. Scores on the RPRS show that patients do not necessarily experience the relationship with the trained professional as facilitating and supportive toward their individual recovery process. Results show no difference between men and women patients during and after the training program was given. Therefore, the results of this study do not support a possible causal relationship between the behavior of the professional toward the patient (working alliance) and the individual recovery process. However, it is shown that the working alliance within the field of psychotherapy is generally seen as effective (Orlinsky, Ronnestad, & Willutski, 2004). Hicks and colleagues (Hicks, Deane, & Crowe, 2012) found it difficult to make a statement about the causal relationship between the working alliance and recovery of severe mental illness; they found that changes in the alliance predicted recovery, but that changes in recovery also predicted the alliance.

With regard to the working relationship, in this study, a complicating factor was that, during the training program, many changes in the care organization were either pending or taking place. For example, more ambulatory treatment was implemented for the severe mentally ill patient. Moreover, patients were treated by different professionals what probably influenced the perceived alliance. This might explain our poor results on the RPRS. The present results indicate that more research on the effect of gender is needed, and on the causality between the working alliance and recovery of severe mental illness.

This study has some limitations that need addressing. First, we know now from the refocus model (Slade, 2009) that the implementation of recovery is much more complex than the recovery-oriented care training program that is offered in this organization. The training program is based on only one part of the REFOCUS program (i.e. staff values, knowledge and partnership) and lacked specific training in working practices. This is a limitation of the training program and therefore a limitation of this study. However, despite this limitation, positive results were found on the MHRM.

Second, regarding the outcomes on the RPRS, this study might have been improved had we been able to investigate specific couples of ‘professional and patient’. This could have offered more insight into changes in the relationship during the training program. However, although this item was fully discussed before starting the evaluation study, it proved impossible because of the way the study was conducted. Moreover, the organization was rapidly changing and not all professionals remained in the same department; all these factors prevented forming stable professional/patient couples over time.

Third, the presented study did not use a control group to compare the results with; therefore, it is unknown whether observed changes can be contributed to the training program.

Lastly, the multiple measurement occasions made the research vulnerable; because six measurements took place, this made it difficult to maintain the cooperation/motivation of the patients. Moreover, the results of this study can also generate more hypotheses to investigate namely, how exactly gender influence the recovery process, what is the difference between men and women in their process to recovery, is there a difference between male and female professionals in attitude and behavior toward male and female patients? These are interesting research questions for future research about recovery.

Strength of this study is that it is the first in which the indirect influence of a recovery-oriented training program for professionals is measured on mental health consumer outcomes. On the other hand, it should be mentioned that the lack of reference data makes some of the results somewhat difficult to interpret.

Conclusion

This study demonstrates that patients with serious mental illness can make a start with their recovery process while professionals were trained or are trained in the recovery vision. However, the results also show that the relationship with the professional is not experienced as a more recovery-oriented one during and after the recovery-oriented training program for professionals. This study generates more research questions and indicates that more research is needed on how patients can actually be empowered by professionals, in which way gender influences the process of recovery, and how the working alliance between the professional and patient influences the recovery of severe mental illness.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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