Well-being and occupational roles among middle-aged women
Bibliographic record
Abstract
One purpose of the present study is to explore the stability of the pattern of health/work and sickness absence among middle-aged women over a period of three years. This study tested two hypotheses: (a) that enduringly healthy working women would perceive more valued occupational roles and higher well-being than long-term sick-listed women; (b) that high levels of well-being at baseline would predict enduring health and occupational role value at a 3-year follow-up. Middle-aged women (n = 208) answered a postal survey with the Role checklist, a well-being scale and questions about work and sickness situation. The results showed that there was a considerable variability in the pattern of health/work and sickness absence. The variability was greatest among the women who were long-term sick-listed at baseline, and the internal drop out was great among them. The results showed that the enduringly healthy women experienced a more valued worker role and higher well-being than the long-term sick-listed women. Furthermore, high levels of well-being concerning health and work predicted enduring health in the studied sample, and high well-being concerning work was predictive of a valued worker role. Interventions that enable women to develop valuable worker and leisure roles, as well as harmony between different roles, may be important constituents of health promotion/rehabilitation programmes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".