Work Characteristics and Personal Social Support as Determinants of Subjective Well-Being
Bibliographic record
Abstract
BACKGROUND: Well-being is an important health outcome and a potential national indicator of policy success. There is a need for longitudinal epidemiological surveys to understand determinants of well-being. This study examines the role of personal social support and psychosocial work environment as predictors of well-being in an occupational cohort study. METHODS: Social support and work characteristics were measured by questionnaire in 5182 United Kingdom civil servants from phase 1 of the Whitehall II study and were used to predict subjective well-being assessed using the Affect Balance Scale (range -15 to 15, SD = 4.2) at phase 2. External assessments of job control and demands were provided by personnel managers. RESULTS: Higher levels of well-being were predicted by high levels of confiding/emotional support (difference in mean from the reference group with low levels of confiding/emotional support = 0.63, 95%CI 0.38-0.89, p(trend)<0.001), high control at work (0.57, 95%CI 0.31-0.83, p(trend)<0.001; reference low control) and low levels of job strain (0.60, 95%CI 0.31-0.88; reference high job strain), after adjusting for a range of confounding factors and affect balance score at baseline. Higher externally assessed work pace was also associated with greater well-being. CONCLUSIONS: Our results suggest that the psychosocial work environment and personal relationships have independent effects on subjective well-being. Policies designed to increase national well-being should take account of the quality of working conditions and factors that facilitate positive personal relationships. Policies designed to improve workplaces should focus not only on minimising negative aspects of work but also on increasing the positive aspects of work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".