Development and validation of the Quality of Work Life Scale for elderly workers

Abstract

BACKGROUND:

With the rapid rise in the elderly population and their labour force participation, quality of work life (QoWL) of elderly workers becomes an important concept. A valid instrument to measure elderly workers QoWL is a prerequisite to further in this direction.

OBJECTIVE:

To develop and validate the Quality of Work Life Scale-Elderly (QoWLS-E) for elderly workers 60 years and above in Sri Lanka.

METHODS:

The development and validation of 35 items in QoWLS-E was carried out in two stages. Using a literature search and expert opinion, the items were developed in English language and later translated to Sinhala language. The initial scale consisted of 38 items and a principle component analysis (PCA) was conducted among 275 elderly workers in selected administrative divisions of Colombo district. Then a confirmatory factor analysis (CFA) was conducted among a separate group of 250 elderly workers to confirm the factor structure of the developed scale.

RESULTS:

PCA identified 9 principle components accounting for a variance of 71%, which was later confirmed in the CFA (RMSEA-0.07, SRMR-1.0, NNFI-0.87, GFI-0.82, CFI-0.96). The final QoWLS-E with a structure of 9 domains namely; physical health, psychological, welfare facility, safety, job content, co-worker, supervisor, flexibility and autonomy having 35 items correlated satisfactorily with Cronbach’s alpha of 0.77 and test – retest reliability of 0.82.

CONCLUSION:

QoWLS-E is conceptually and culturally appropriate to assess Quality of Work Life Scale in elderly. It could be a useful tool to describe and monitor improvement of QOWL in elderly.

Keywords

Quality of work life measure exploratory factor analysis confirmatory factor analysis

1 Introduction

The world’s elderly population is growing rapidly and in 2050 one in six persons in the world will be over 65 years and above [1]. According to the World Health Organization (WHO) from 2015 to 2050 the world population over 60 years will rise from 12% to 22% [2]. The elderly population in developing countries is also increasing at a rapid rate and the Sri Lankan population structure follows a similar trend. Sri Lanka is expected to have 6.3 million (27.4%) of elderly people aged more than 60 in 2050 [3].

The average regional total fertility rate of 5.5% in Southeast Asia in 1970 declined to 2.4% in 2015, representing an increased elderly population [4]. This transition marked a decrease in the traditional working age population, thus an increased old age share in the labour force. The projections show that the ratio of elderly people to the people of working age will rise from 1 in 4 in 2015 to 1 in 2 in 2050. In the United States of America, those over 55 are the fastest growing segment in the workforce. By 2030 it is expected that the workers 75 years and older to rise by 11.7% [5]. According to Chartered Institute of Personnel and Development [6], in 2015 over 30% of the United Kingdom workforce was aged 50 and above. Organization for Economic Co-operation and Development (OECD) projects that aging will be a major social and economic challenge. Further, they propose the decline in the labour force can be reduced if the older workers delay their effective age of retirement [7]. Also, the empowerment of older persons to fully and effectively participate in economic, political and social lives of their societies including income generation and voluntary work was set as a goal in the Madrid International plan [8]. Studies have shown engagement of elderly population in the workforce is a significant predictor of survival (mortality odds ratio-0.4) among the older workers compared to non-workers [9] and unemployed workers had a poor health outcome than the employed older workers (OR = 2.75, 2.46–3.07) [10].

Sri Lanka has already identified the importance of the elderly workforce. The National Charter for Senior Citizens of Sri Lanka [11] has stated that opportunities should be given to willing and capable elderly workers to engage in employment. At present, 25% of the elderly population are economically active [12] and provide a significant contribution to country’s economy.

Hence, it is important to attract and retain elderly workers, and for this, maintaining a good quality in their work life is vital. Knowledge on the level of Quality of Work Life (QoWL) of elderly workers would help to identify issues specific to elderly workers and to intervene for further improvement.

The QoWL is a multi-dimensional concept which was originated in 1930 [13]. It is the employee satisfaction on personal and working needs through participating in the work while achieving the goals of the organization [14]. This concept had been extensively studied by occupational psychologists, experts in human resource management, public health experts etc. among working age populations. Quality of Work Life has an objective component which includes work characteristics and subjective component as workers satisfaction [15]. Different theories and models had been used to explain this concept [16, 17] and some of the main dimensions of QoWL are safe and healthy work environment, organizational culture, relation and co-operation, health & well-being, training and development, compensation and rewards, facilities, job satisfaction, job security, autonomy of work, adequacy of resources, work life balance and time pressure [14 , 17–19].

The quality of the job and the environment is very important for any age of a worker. However, the QoWL of elders have common dimensions with the young workers as well as compositional differences [20]. Elderly workers’ expectations regarding a job is likely to differ from that of a younger worker. The occupational features where the elderly workers usually value are; flexibility, personal meaningful, intellectually stimulating, sociable and offers adjustments for the disabilities and the health conditions. They also consider the employer support and the importance of their work for the organization. They will be looking for strong work place relationship and autonomy too [21].

Even though there are several tools to measure QoWL in general, they may not capture the true QoWL of elderly workers due to the unique nature of the needs of elderly. With the increasing number of elderly joining the workforce, their quality of work life becomes important. Hence, this study intended to develop and validate a tool to assess the QoWL of elderly workers.

2 Methods

The tool development was done in three main phases, namely: (i) identification of tentative domains and item generation, (ii) development of QoWLS – E and (iii) evaluation of QoWLS-E.

In the absence of a universally accepted definition for elderly workers and QoWL for elderly workers, the following definitions were developed through Delphi technique involving a panel of subject experts which included specialized professionals in Occupational Health, Geriatrics, Administration, Sociology, as well as selected elderly workers. Elderly worker was defined as, “A worker who is aged 60 years or above, who had been working for the last six months, for a minimum of twenty hours per week at the time of recruiting for the study. The worker may belong to any skill level and can be a paid regular employee or a self – employed person who works for a wage or salary in cash or in kind. Casual workers and seasonal workers were excluded”. Quality of Work Life of an elderly worker was defined as, “A state in which the elderly worker achieves optimal physical and psychological health preserving the elderly workers autonomy through suitable job characteristics while developing human capacities and work life balance to achieve optimal job satisfaction”.

2.1 Identification of tentative domains and item development

Both deductive and inductive methods were used for item generation. Based on deductive method items were generated from extensive literature search and assessment of available tools. The inductive method helped to extract the relevant items and domains were identified from the discussion with the panel of experts consisting of specialists in public health, managers, sociologists, psychologists and elderly workers [22]. Afterwards, possible items were identified, duplicates were removed and a common item list was prepared. The selected items were converted into unambiguous simple statements. The face and content validity were assessed by a panel of experts and a selected group of elderly workers. During this process the relevance of content, representativeness and technical quality was assessed by the experts individually using a predetermined rating scale. Consensus was reached between the experts using the modified Delphi technique. These statements were originally drafted in English and later translated into Sinhala language. Since the education levels of the elderly workers differ, the tool was planned to be an interviewer administered tool [23]. The responses for the QoWLS-E were recorded in five-point Likert scale.

2.2 Scale development

The scale was pretested with 20 elderly workers and the relevance of the questions to the concept of QoWL in elderly workers as well as whether the answers produce a valid measurement were assessed. The modified scale was used for the data collection for the exploratory factor analysis to identify the domain structure of the newly developed scale. Using multistage sampling [24] the study participants were identified from the elderly workers who fit for the working definition and residing more than one year in the study area, while excluding the workers who were acutely ill or could not be contacted after 3 visits. Multistage cluster sampling was used where a cluster was defined as a Gramasevaka Niladhari Division (GND) from which 10 elderly workers were enrolled with the support of the field health care workers for the study. The GND is the primary administrative unit of Sri Lanka and these GND’s were identified from the selected Divisional Secretariat Division (DSD) which is the secondary level of administrative units in a district. This DSD selection represented the urban and rural settings, all ethnicities, occupational groups and skill levels [25] of Colombo district.

The data collected from 275 elderly workers, in 29 GND’s of three DSD’s in Colombo district, were subjected to Exploratory Factor Analysis (EFA). A questionnaire on socio-demography of the participants and the developed QoWLS-E were the data collecting instruments. To obtain reliable results, sample size was calculated based on seven respondents per item in the questionnaire [26].

Trained sociology graduates interviewed the participants at their residences after obtaining informed written consent. The necessary administrative clearances were obtained from local administrative and health officials and ethical approval was granted by the university ethics review committee (P/81/02/2018).

All 38 items in the tool subjected to PCA using SPSS 21 and prior to the PCA any violations of assumptions such as normality, linearity, inter-item correlation, and multi-collinearity were assessed. The extraction of the factor structure was done by subjecting the drafted QoWLS-E to Principal Component Analysis (PCA) with varimax rotation. The retained items in each domain had an eigen value greater than 1. Following PCA the 38 items were reduced to 35 items organized into 9 domains.

2.3 Scale evaluation

The Confirmatory Factor Analysis (CFA) was conducted to verify the dimensionality of the newly developed tool. The field study included 250 elderly workers from randomly selected 27 GND in the same DSD’s. The GND’s used for the EFA study were excluded, but the same sampling method was followed when collecting data for the CFA. The newly developed QoWL-S and questionnaire on the socio demographic were the data collection instruments and were administered by using trained data collectors at the participants resident. The data was collected from November 2018 to December 2018.

The CFA of the 35 items in the QoWLS-E were analyzed using Liseral 8.8 and the extent to which the data fitted the nine domains hypothesized in the PCA was confirmed. During the CFA, Robust Maximum Likelihood estimation method was used, and model fitness was assessed by three categories of fitness indexes; Absolute fit, Incremental fit and Parsimonious fit. Cronbach’s alpha coefficient of the Test re-test reliability assessed the internal consistency of the tool and values for Cronbach’s alpha were obtained for each domain and for the total tool.

2.4 Development of cut-off values

Since there is no ‘gold standard’ measure for QoWL in elderly workers, the cut-off values were developed considering the given highest and the lowest scores for the relevant domains by a panel of experts. Each answer in the QoWLS-E was given a score out of 5. Selected statements (2,3,9,20,21 and 33) were scored inversely as they were correlated with poor QoWL. Cut-off points for the overall tool as well as to each domain were decided by the same panel through modified Delphi technique.

3 Results

A total of 43 items were selected for the scale from the item pool after removing 5 duplicating items. The drafted QoWLS-E which had 38 items were used for the scale development phase and the socio demographic details of the participants selected for scale development through PCA are shown in Table 1.

Table 1
Distribution of the study sample of PCA by their socio-demographic characteristics (N = 275)

Socio-demographic characteristic Frequency Percentage (%)

Age category (years)

60–64 122 44.36

65–70 74 26.91

70–74 54 19.64

75 above 25 9.09

Sex

Male 175 63.64

Female 100 36.36

Ethnicity

Sinhalese 256 93.09

Other 19 6.91

Religion

Buddhism 233 84.73

Other 42 15.27

Highest level of education

Not attended to school 17 6.18

Up to year 5 40 14.55

Up to GCE (ordinary level) 95 34.55

GCE O/L passed* 68 24.73

GCE A/L passed ** 50 18.18

Degree and above 05 1.82

Current occupation category–major groups

Legislators, senior officials and managers 04 1.45

Professional 13 4.73

Technicians and associate professionals 15 5.45

Clerks 08 2.91

Service workers and shop and market sales workers 86 31.27

Skilled agricultural and fishery workers 13 4.73

Craft and related workers 51 18.55

Plant and machine operators and assemblers 24 8.73

Elementary occupations 61 22.18

Skill level of current occupation

Skill level 1 61 22.18

Skill level 2 170 61.82

Skill level 3 30 10.91

Skill level 4 14 5.09

Duration engaged in the current job

More than 5 years 197 71.64

Less than 5 years 78 28.36

Socio-demographic characteristic	Frequency	Percentage (%)
Age category (years)
60–64	122	44.36
65–70	74	26.91
70–74	54	19.64
75 above	25	9.09
Sex
Male	175	63.64
Female	100	36.36
Ethnicity
Sinhalese	256	93.09
Other	19	6.91
Religion
Buddhism	233	84.73
Other	42	15.27
Highest level of education
Not attended to school	17	6.18
Up to year 5	40	14.55
Up to GCE (ordinary level)	95	34.55
GCE O/L passed*	68	24.73
GCE A/L passed **	50	18.18
Degree and above	05	1.82
Current occupation category–major groups
Legislators, senior officials and managers	04	1.45
Professional	13	4.73
Technicians and associate professionals	15	5.45
Clerks	08	2.91
Service workers and shop and market sales workers	86	31.27
Skilled agricultural and fishery workers	13	4.73
Craft and related workers	51	18.55
Plant and machine operators and assemblers	24	8.73
Elementary occupations	61	22.18
Skill level of current occupation
Skill level 1	61	22.18
Skill level 2	170	61.82
Skill level 3	30	10.91
Skill level 4	14	5.09
Duration engaged in the current job
More than 5 years	197	71.64
Less than 5 years	78	28.36

GCE O/L* – General Certificate of Education Ordinary Level. GCE A/L**– General Certificate of Education Advanced Level.

Calculation of skewness and kurtosis values revealed the non-normality of the data set. A linear relationship was observed in all the bi-variate scatter plots. The adequacy of sample size was evaluated by KMO values (0.7) and Chi square value for Bartlett’s test of sphericity was 6452.35 with df of 703 and the p value was < 0.001. All the standardized scores were within the mean+/–3.29 value range. Following the PCA of the data set, 35 items remained and 12 domains were identified which were later combined into 9 meaningful domains based on the similarity of the concepts measured (Table 2). These domains were named accordingly.

Table 2

Rotated solution with factor loading

Item	Component
	1	2	3	4	5	6	7	8	9	10	11	12
Have good relationship with supervisor	.942	.138	.019	.026	.094	.079	.006	–.063	.128	.012	–.035	–.040
Have good support from supervisor	.937	.154	.029	.005	.091	.092	.010	–.013	.131	.014	–.028	–.039
Good feedback from supervisor	.943	.161	.021	.004	.072	.081	.015	–.019	.065	.043	–.037	–.028
Have due evaluation	.937	.115	.010	.013	.050	.086	.044	–.052	.075	.019	–.062	–.016
Basic facilities given	.295	.838	.105	–.015	–.056	.049	.063	.062	–.006	.024	–.039	.046
Health facilities are achievable	.347	.834	.107	–.058	.027	–.061	–.029	.039	–.014	–.033	.031	.081
Well-fair facilities given	.445	.71	.019	.098	–.136	.135	.123	–.026	–.011	.099	–.010	–.058
Necessary equipment’s hare available	.006	.604	–.146	–.126	.207	–.025	.063	–.044	.167	.226	.035	–.070
Job cause psychological fitness	.014	.031	.934	.066	–.002	.047	.082	.099	.014	.018	.035	.000
Job is satisfying	.005	.031	.909	.169	–.012	.089	.083	.122	.051	.071	.048	.010
Job task is clear	.185	–.139	.231	–.30	–.055	.197	.163	–.150	.336	.090	–.119	.010
Motivated to work well	.061	–.022	.352	.628	–.083	.163	.287y	–.173	.178	.132	–.031	.112
Job is useful to the society	.003	.042	.223	.520	.022	.057	.203	.205	.071	.185	.305	.105
Have less time with family work	–.105	–.155	–.143	.643	–.068	.002	–.034	.079	–.156	–.167	–.061	–.071
Job is monotonous	.064	.113	–.170	.327	.054	.152	–.028	–.115	.037	.039	–.652	.014
Have opportunity to improve	.013	.202	.157	.466	.013	–.065	–.050	–.019	.133	.098	.338	–.160
Have good co-worker relationship	.154	.016	–.025	–.050	.958	.127	.008	.068	.011	.006	–.012	.029
Have a good support from co-workers	.162	.016	–.004	–.041	.957	.120	.024	.069	.003	.022	–.009	.045
Can express views on decision making	.210	.035	.065	.087	.145	.926	.046	–.071	.090	.053	–.033	.023
My views taken regarding my job	.215	.021	.072	.075	.120	.925	.049	–.095	.049	.061	–.011	.022
I’m happy with the iob	–.062	.085	.439	.016	–.063	–.110	.633	.126	.077	.076	.129	–.072
Satisfied with the physical environment	–.016	.051	–.017	.111	.099	.074	.817	.020	.129	.077	–.097	–.033
Looking for another job	–.177	–.080	–.153	–.197	.034	–.174	–.482	.286	–.033	–.079	.031	.253
Illnesses are barrier to the work	.067	–.025	–.114	.175	–.081	.114	–.107	–.77	–.066	.023	.001	–.201
Joint pains are barrier	–.035	.031	–.020	–.208	–.002	.155	.333	–.643	.012	–.056	.031	.154
Age is not a barrier	–.082	.021	.260	–.008	.061	.091	.198	.532	.248	.130	.253	–.197
Job cause physical fitness	–.066	.115	.158	.123	.138	.071	.134	.476	.158	–.111	.081	–.497
Safe work environment	.085	.118	–.005	.071	–.047	–.031	.215	.181	.660	.066	–.058	.332
Can perform safely	.089	.007	.031	.124	.036	.146	.048	.092	.769	.063	.093	–.041
No fear of loosing the iob	.175	.016	.277	–.191	.068	–.058	–.144	–.204	.449	–.377	.266	–.141
Have feasible work time	.412	.224	–.055	.043	–.036	.138	.052	.073	–.030	.634	–.103	–.010
Can have a leave when need	–.006	.017	.091	.015	.038	.061	.090	–.023	.094	.754	.093	.041
Have to work hard	.217	–.087	–.151	.303	–.257	.027	.342	.051	–.160	–.51	.203	.070
Skill is matched with the job	–.042	.055	–.058	.214	.012	.069	–.044	.007	.083	.032	.771	.054
No time pressure	–.124	.048	.062	.018	.111	.074	–.081	.013	.142	–.005	.041	.770
Salary is adequate	.100	.155	.119	.206	–.094	–.154	.237	.116	–.135	.347	.202	–.017
Taking frequent leave as I don’t like the job	–.108	–.018	–.126	–.340	–.076	–.046	–.190	.335	–.251	.219	.179	.283
Equally paid	.668	.308	–.062	.023	.095	.143	–.041	–.027	–.197	.049	.102	–.034
Extraction method: Principal component analysis. Rotation method: Varimax with Kaiser normalization.^a

^aRotation converged in 16 iterations.

The 9-factor model, along with two other theoretically-plausible models–an eight-domain model and a ten-domain model, underwent scale validation using confirmatory factor analysis in a separate sample. The socio demographic details of the study participants of scale validation step is presented in Table 3. The models with 8, 9 and 10 domains were assessed for model fitness using CFA (Table 4). A satisfactory level of goodness of fit was obtained for the model with 9 domains. The Nine – Domain model has a chi – square value of 1182.48 and the Root Mean Square Estimate of Approximation value of 0.07. The Comparative Fit Index, Goodness of Fit Index and Parsimony Normed Fit Indexes were respectively 0.96, 0.51 and 0.58. The path diagram of the final 9-domain model is given in Fig. 1 and the final structure of the tool is shown in Table 5.

Table 3

Socio – demographic characteristics of the study population of CFA (N = 250)

Characteristic	Number	Percentage (%)
Age categories (years)
60–64	83	33.20
65–69	70	28.00
70–74	63	25.20
75 and above	34	13.60
Sex
Male	115	46.00
Female	135	54.00
Ethnicity
Sinhalese	245	98.00
Other than Sinhala	05	2.00
Highest level of education
Not gone to school	08	3.20
Up to grade 5	35	14.00
Up to GCE O/L	104	41.60
GCE O/L passed^*	34	13.60
GCE A/L passed^**	42	16.80
Basic degree	07	2.80
Other	20	8.00
Current occupation category according to the major groups
Legislators, senior officials and managers	09	3.60
Professional	12	4.80
Technicians and associate professionals	36	14.40
Clerks	02	0.80
Service workers and shop and market sales workers	70	28.00
Skilled agricultural and fishery workers	15	6.00
Craft and related workers	41	16.40
Plant and machine operators and assemblers	13	5.20
Elementary occupations	52	20.80
Skill level of the current job
Skill level 1	62	24.80
Skill level 2	125	50.00
Skill level 3	49	19.60
Skill level 4	14	5.60
Duration in the current job
More than 5 years	66	26.40
Less than 5 years	184	73.60

GCE O/L * – General Certificate of Education Ordinary Level. GCE A/L**– General Certificate of Education Advanced Level.

Table 4

Model fit statistics of the ten domain, eight domain, and nine domain models

Model	Absolute fit indices							Comparative fit indices		Parsimony fit indices
	χ ²	Df	P	RMSEA	GFI	AGFI	SRMR	NNFI	CFI	PGFI	PNFI
Nine-domain	1182.48	524	0.000	0.07	0.82	0.72	0.10	0.87	0.96	0.51	0.58
Eight-domain	1283.44	532	0.000	0.08	0.80	0.71	0.10	0.72	0.78	0.54	0.59
Ten-domain	1182.01	515	0.000	0.07	0.84	0.73	0.08	0.74	0.80	0.53	0.59

Fig. 1

Path diagram of nine domain model.

Table 5

Developed QoWLS-E: Final structure of the tool with items and domains

Statements/items	Domain
1. My age is not a barrier for my job.	Physical
2. My physical illnesses are barriers for my work.	Physical
3. My joint pains are a barrier for my work.	Physical
4. At this moment I feel less physical difficulties because of my work.	Physical
5. At this moment, my job gives me psychological relief.	Psychological
6. Usually I enjoy my job.	Psychological
7. I enjoy the work that I’m doing in my job.	Psychological
8. I’m satisfied with my working environment.	Psychological
9. At this moment I’m looking for another job instead of this one.	Psychological
10. Other than the salary, from the job I get benefits such as free food, free transport.	Welfare facilities
11. I have been give toilet facilities and dining facilities/ Basic facilities.	Welfare facilities
12. Because of my job, I get chances for health facilities.	Welfare facilities
13. At my work place I have the necessary equipment to do my job.	Welfare facilities
14. My work environment is safe.	Safety
15. I can perform my work safely at my work station.	Safety
16. I don’t have a fear of losing my job.	Safety
17. I have a clear idea about my duties.	Job content
18. I’m motivated to do my work well.	Job content
19. My job gives me an opportunity to practice things that are important to my job and my life.	Job content
20. My job reduces the time I have to spend with my family members.	Job content
21. My job is a monotonous one.	Job content
22. I feel that I’m doing a useful job to the society.	Job content
23. I have enough time to finish the given work for me.	Job content
24. My job is matching with my skill.	Job content
25. I have a good relationship with my coworkers.	Co – worker
26. I get good support from my workmates.	Co – worker
27. I have a good relationship with my supervisor/s.	Supervisor
28. I get good support from supervisor/s.	Supervisor
29. I get adequate ideas, guidance &feed-back from my supervisor/s.	Supervisor
30. I get the deserved appreciation from my supervisors.	Supervisor
31. I can adjust my work time according to my wish.	Flexibility
32. I can take sudden leave for my needs.	Flexibility
33. According to my job I have to work hard.	Flexibility
34. I have an opportunity to express my view regarding work place decisions.	Autonomy
35. My views are considered in taking the decisions related to my work.	Autonomy

The mean domain scores and Cronbach’s α coefficients are presented in Table 6. The measure of internal consistency ranged from 0.63 to 0.89 and the internal consistency reliability of the entire instrument was 0.768. The test re-test reliability was assessed with a sub sample of 50 participants and the total correlation coefficient was 0.82.

The cut–off marks developed for the scale are shown in Table 7. There were 9 domains and arithmetic sum of each domain score gave the cut off mark for the total scale. The possible scores in the scale ranged from 35–175. The cut-off mark for the scale was decided as 126 and scores above this represents the higher QoWL.

Table 6

Reliability and internal consistency of the domain

Domain	Internal consistency within domain	Correlation coefficient
Physical factor	0.77	0.82
Psychological	0.73	0.73
Welfare facilities	0.78	0.81
Safety	0.71	0.84
Job content	0.70	0.85
Co – worker	0.76	0.87
Supervisor	0.79	0.85
Flexibility	0.77	0.86
Autonomy	0.74	0.84

Table 7

Cut off mark development for the scale

Domain	Number of items in the domain	Cut off mark for the domain given by the panel of expert	Range of scores for each domain
1. Physical health	04	15	04–20
2. Psychology	05	18	05–25
3. Welfare facility	04	14	04–20
4. Safety	03	10	03–15
5. Job content	08	27	08–40
6. Co-worker	02	08	02–10
7. Supervisor	04	15	04–20
8. Flexibility	03	11	03–15
9. Autonomy	02	08	02–10

4 Discussion

Principle component analysis enabled the identification of the relationship between the items and the domains of the tool. These relationships were further confirmed in a separate sample through confirmatory factor analysis. Identified domains were well aligned with the concept of QoWL of elderly workers. The identified domains in this tool are namely; physical health, psychological health, facility, safety, job content, co-worker, supervisor, flexibility and autonomy.

Construct of the QoWL had been measured using a variety of tools in different contexts [16]. However, with the specific physical, psychological and social needs of the elderly persons, it is unlikely that the general QoWL measures would effectively capture the situation among elderly workers. In a rapidly aging world, more and more elderly persons are expected to join the labour force. In this background, adapting the concept of QoWL to elderly workers is important and this study aimed to develop and validate a new tool to assess the QoWL of elderly workers in Sri Lanka.

Valid operational definitions for ‘elderly worker’ and the ‘QoWL of elderly workers’ for the Sri Lankan context were achieved by scientifically incorporating the inputs of a multi–disciplinary panel of experts. A widely used non–statistical method of expert rating was used to refine the pool of generated items [27]. Due to the diverse socio-economic levels, applied literacy and physical disabilities in the elderly workers, QoWLS-E was developed as an interviewer administered questionnaire (IAQ) to minimize the non-response bias. However, IAQ’s carry the inherent limitation of response bias where the respondent could provide inaccurate information to be more socially acceptable [28]. Therefore, training the data collectors, and asking clear questions were employed to minimize bias. The scale was developed in simple language which is more suitable to collect data when the study population is having varying levels of education [29].

QoWL in the elderly being a novel concept without a reference measure, the validity and the reliability of the QoWLS-E were assessed using triangulation of several methods. Since QoWLS-E was a new tool, PCA was carried out as recommended by Redding and colleagues [27], which provides the best option in identifying the underlying constructs [30]. Construct validity of the tool is further ensured by conducting CFA in a separate sample, which confirmed the validity of the factor structure identified through PCA.

The identified domains in this tool namely; physical health, psychological environment, welfare facilities, safety, job content, co-worker relationship, supervisor relationship, flexibility and autonomy, were all consistent with the existing theoretical concepts of QoWL [16, 17]. Though most of the available QoWL tools have similar domains or items [14, 31] this new QoWL-E was clearly more specific on geriatric work-related issues. Physical health emerged as a separate domain; the elderly workers’ physical health related issues were predominantly assessed through this tool. In addition, welfare facilities, specifically receiving health facilities from work place also emerged as an important area for QoWL. Flexibility in the job with regard to working times and days and autonomy of the elderly worker in making decisions, were also important domains that showed a greater value to the elderly workers. These unique features make the tool suitable to identify geriatric specific work-related issues.

Acceptable internal consistency reliability requires Cronbach’s α values of 0.7 [32]. The high internal consistency and test-retest reliability shown in the current study were similar to the other comparable multidimensional measures such as QoWL scale developed by Van Larr and team and Swamy & Nanjundeswarawamy’s QoWL tool. High internal consistency across the 35 items suggests that all the items together measure the same construct of QoWL.

Both Van Laar and team’s QoWL scale [31] and Swamy & Nanjundeswarawamy’s QoWL tool [16] were developed based on occupational settings but the present tool was developed based on the community setting. Since the current tool aimed to cover a diverse range of work settings, the number of domains extracted in the present scale was high compared to the scale developed by Van Laar and team. This permits the use of QoWLS-E to assess the QoWL of organizational based workers as well as self – employed workers, both in work settings as well as community settings. The study was based in a capital district of Sri Lanka which has a wide diversity of occupations hence, QoWL-E is capable of assessing the QoWL covering a wide range of the workforce.

The items included in QoWLS-E were largely specific for the QoWL in the elders and the number of items was not high (n = 35). Also, the administration time of the tool was 20 minutes on average. All these factors enhanced the applicability and the compliance of QoWLS-E for the elderly workers and it was proven by the high response rate.

5 Conclusions

A 35-item multidimensional scale (QoWLS-E), consisting 9 domains has shown good construct validity and reliability. QoWLS-E therefore, shows much promise as a tool to describe QoWL in elderly workers as well as an outcome measure of future interventions to enhance it. This will support generating evidence to retain much needed elderly workers in the labour force while improving their work-related wellbeing.

Ethical approval

Ethical approval was obtained from the Ethics Review Committee of the Faculty of Medicine, University of Kealniya, Sri Lanka (P/81/02/2018). Consecutive approval was obtained from the same Ethics Review Committee to continue the data collection.

Informed consent

Written informed consent was obtained from each participant prior to data collection.

Footnotes

Acknowledgments

Not applicable.

Funding

Not applicable.

References

United Nations. World Population Ageing 2019 Highlights. In Population Division of the Department of Economic and Social Affairs; 2019.

World Health Organisation. Ageing and Health; Fact Sheet. 2021.

De Silva

. The Age Structure Transition and the Demographic Dividend: An Opportunity for Rapid Economic Take-off in Sri Lanka. Sri Lanka Journal of Advanced Social Studies. 2013;2(1).

The Economist Intelligence Unit Limited. The Disappearing Work Force? Why countries in Southeast Asia need to think about fertility rate before it’s too late. 2019.

U.S.A. Bureau of Labor Statistics. The Economics Daily, Number of people 75 and older in the labor force is expected to grow 96.5 percent by 2030. 2021; November 04.

Charted Institute of Professional Development. Avoiding the demographic crunch: Labour supply and the ageing workforce. 2015.

Organization for Economic Co-operation and Development. Council Recommendation on Ageing and Employment (Issue October). 2018.

United Nations Political Declaration and Madrid International Plan of Action on Ageing. 2002.

Blanc

, Katz

, Yelin

. Mortality risk among elderly workers. American Journal of Industrial Medicine. 1994;26:543–7.

10.

Kachan

, Fleming

, Christ

, Muenning

, Prado

, Tannenbaum

, Yang

, Caban-Martinez

, Lee

. Health Status of Older US Workers and Non workers, National Health Interview Survey, 1997–2011. Preventing Chronic Disease. 2015;12:162.

11.

Ministry of Social Services and Welfare. National Charter for Senior Citizens and National Policy for Senior Citizens Sri Lanka. 2006.

12.

Department of Census and Statistics. □ □ □ □ □ □ □ □ □ □. 2015.

13.

Moradi

, Maghaminejad

, Azizi-Fini

. Quality of working life of nurses and its related factors. Journal of Nursing and Midwifery Studies. 2014;3(2):19450.

14.

Swamy

, Nanjundeswaraswamy

, Rashmi

. Quality of Work Life: Scale development and validation. International Journal of Caring Sciences. 2015;8(2):281–300.

15.

Steffgen

, Kohl

, Reese

, Happ

, Sischka

. Quality of Work: Validation of a New Instrument in Three Languages. International Journal of Environmental Research and Public Health. 2015;12(12):14988–5006.

16.

Walton

. Criteria for quality of work life. In Quality of working life: problems, projects and the state of the art. 1975;91–104. Macmillian.

17.

Sirgy

, Efraty

, Phillip

, Lee

. A New Measure of Quality of Work Life (QWL) Based on Need Satisfaction and Spillover Theories. Social Indicators Research. 2001.

18.

May

, Lau

, Johnson

. A longitudinal study of quality of work life and business performance. South Dakota Business Review. 1999;58(2):1–7.

19.

Mehrotra

, Khandelwal

. Association of Demographic Variables (Gender and Salary) On Quality of Work Life of Teaching Employees in Private Technical Institutions in Bareilly Region. International Journal of Education and Science Research Review. 2015;2(2):2348–6457.

20.

Steiber

. Encyclopedia of Quality of Life and Well-Being Research. 2014, Pages 111–116.

21.

Marell

, Cox

. What do older workers value about work and why? Center for Ageing Better. 2017.

22.

Boateng

, Neilands

, Frongillo

, Melgar-Quinonez

, Young

. Best Practices for Developing and Validating Scales for Health, Social and Behavioral Research: A Primer. Frontiers in Public Health. 2018;6:149.

23.

Streiner

, Norman

, Cairney

. Health Measurement Scales. 2015;5. Vol. 1. Oxford University Press.

24.

Siriwardhana

, Walters

, Rait

, Bazo-Alvarez

, Weerasinghe

. Cross-cultural adaptation and psychometric evaluation of the Sinhala version of Lawton Instrumental Activities of Daily Living Scale. PLoS One. 2018;13(6):e0199820.

25.

International Labour Office. International Standard Classification of Occupations: ISCO-08.2012.

26.

Kline

. An Easy Guide to Factor Analysis. 1994. Routledge.

27.

Redding

, Maddock

, Rossi

. The Sequential Approach to Measuremnet of Health Behavior Constructs:Issues in Selecting and Developing Measures. Californian Journal of Health Promotion. 2006;4(1):83–101.

28.

Lavrakas

. Encyclopedia of Survey Research Methods. 2008. Sage Publications, Inc.

29.

Abramson

, Abramson

. Survey Methods in Community Medicine. 5th ed. Edinburgh, United Kingdom: Churchill Livingstone; 1999.

30.

Stapleton

. Basic Concepts in Exploratory Factor Analysis (EFA) as a Tool to Evaluate Score Validity: A Right – Brained Approach. Paper presented at the Annual Meeting of the Educational Research Association, Austin, TX, USA. 1997.

31.

Van Laar

, Edwards

, Easton

. The Work-Related Quality of Life scale for healthcare workers. Journal of Advanced Nursing. 2007;60(3):325–33.

32.

Heale

, Twycross

. Validity and reliability in quantitative studies. Evidence-Based Nursing. 2015;16(9).