Design and sample
Ten to Men is a national cohort study of Australian males [28, 29]. Wave 1 data collection took place from October 2013 to July 2014, resulting in detailed information being provided by 15,988 males aged 10–55 living in close to 14,000 households. Wave 2 data collection has begun and was completed in June 2016. The response fraction at Wave 1 was 35 % of confirmed in-scope males.
The cohort was recruited via a stratified, multi-stage, cluster random sampling strategy that involved approaching eligible males residing in private dwellings, with separate cluster samples drawn from regional strata to ensure over sampling of males from regional areas. All private dwellings in sampled areas were enumerated and all males within the target age range in those dwellings were invited to participate. Interviewers collected household-level information including details of all males in the household regardless of whether they were participating or not. All participants provided informed written consent. Data were collected by personal interview for males aged 10–14, and self-complete paper hard copy questionnaire for males aged 15 years and older. The questionnaires covered a range of dimensions including social, demographic, health and economic conditions . The analyses presented in this paper are restricted to males aged 18–55 at baseline as these participants would have had the opportunity to complete secondary education and to participate in the labour market. The Human Research Ethics Committee at the University of Melbourne approved the pilot studies and the main Wave 1 data collection.
We used two outcome measures for this analysis: The Personal Wellbeing Index – Adult (PWI-A) and the Mental Component Summary (MCS) of the Short Form 12 (SF-12). The PWI-A is a multi-item scale designed to measure subjective wellbeing that has been widely used to characterise and monitor population level wellbeing in Australia. It contains seven items about satisfaction, each one corresponding to a quality of life domain, and measured on an 11-point Likert scale (from 0, completely dissatisfied, to 10, completely satisfied): standard of living, health, achieving in life, relationships, safety, community-connectedness, and future security . The seven items are summed to provide an overall score. These domains represent the deconstruction of satisfaction with ‘life as a whole’. Notably, satisfaction with job or work is not included, which makes the scale applicable to the full adult population including the unemployed and those not in the labour force due to caring responsibilities, disability, retirement, or other reasons. The range of the PWI-A is from 1–100, with 100 representing optimal overall life satisfaction. The seven domains load consistently on a single stable factor that account for 50 % of the variance in Australian samples and the Cronbach’s alpha lies between 0.70 and 0.85 . The mean score on the PWI-A in Wave 1 of Ten to Men was 70.33 with a standard deviation of 17.23.
The MCS is a summary measure of mental health derived from the SF-12 health survey . The SF-12 is a widely used measure of health status, has been validated for use in the Australian population . The scores are standardised to a mean of 50 and an SD of 10 (range from 1–100), with 100 representing optimal functioning and mental health. The MCS mean score in the Ten to Men Wave 1 was 49.89, with a standard deviation of 9.23.
The psychosocial job characteristic items included in Ten to Men survey (control, demands and complexity, job insecurity, unfair pay and overall psychosocial job quality) were used to compute a multidimensional measure of psychosocial job quality. Previous publications document the full details of the construction and validation of the job quality measure [9, 11] as well as its use by our investigator group in other studies [12, 33]. In brief, factor analysis and structural equation modelling identified three separate factors, which were labelled: job demands and complexity (three items, alpha = 0.70, e.g.: “my job is complex and difficult”); job control (three items, alpha = 0.64, e.g.: “I have freedom to decide how I do work”); and perceived job security (three items, alpha = 0.82, e.g.: “I have a secure future in my job”). An additional single item assessing whether respondents considered that they were paid fairly for their efforts at work (“I get paid fairly for the things I do in my job”) was included as a fourth factor measuring one aspect of effort-reward imbalance. Each of these four individual factors was associated with more widely used measures of job demands and control, and other employment conditions such as casual status, hours worked and shift work .
To create the overall psychosocial job quality measure, each factor was dichotomized to identify the quartile experiencing the greatest adversity and the composite measure was constructed by summing the number of adverse psychosocial job conditions (high job demands and complexity, low job control, high job insecurity and unfair pay). Because of the small number of respondents reporting all four job adversities, this composite scale was top-coded at three, yielding categories of optimal jobs (no psychosocial adversities), and one, two, and three or more psychosocial adversities (poorest quality jobs).
Potential confounders of the job stressor—mental health/wellbeing relationships were selected on the basis of past literature. These included age (measured continuously, 18–55 years), occupational skill level (low [sales, machinery workers, and labourers], medium [technical and trade workers, community and personal service workers, and clerical and admin workers], and high [managers and professionals] according to the Australian and New Zealand Standard Classification of Occupations occupational groupings ), employment arrangements (permanent, casual or labour hire, fixed term or self-employed), working hours in main job (up to and including 40 h, over 40 h)  and presence of disability or long term health condition (yes or no) using the Washington Group Disability questions .
Variables were summarised descriptively using frequencies, means, and standard deviations. We analysed data on all adult males with valid measures for the PWI-A, MCS, and psychosocial job quality (employed males, aged 18–55 years at baseline). We modeled the corsspsectional association between psychsocial job stressors and the PWI-A and MCS outcome separately using multiple linerar regression. Firstly, we modelled PWI-A (model 1) and MCS (model 2) as outcomes in relation to the four individual psychosocial job stressors as continuous variables (job control, job demands, job security and fairness of pay, mutually adjusting each for the others). Secondly we modelled the PWI-A (model 3) and MCS (model 4) outcomes in relation to overall psychosocial job quality (as an ordinal variable reflecting 1 adversity, 2 adversities, 3 or more adversities, considered relative to the omitted reference category of optimal job quality). We controlled for confounding by including a number of relevant covariates (age, occupational skill level, employment arrangements, working hours and presence of long term health condition or disability and education).