Understanding return to work behaviours: promoting the importance of individual perceptions in the study of return to work
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to demonstrate and discuss how individuals' subjective perceptions of personal and environmental issues influence return to work behaviour. METHOD: A qualitative design utilizing in-depth interviews and maximum variation sampling of 11 individuals who either returned to work or withdrew from work after a health leave was conducted. Experiences elicited were analysed using the constant comparative method followed by a member check with participants to confirm findings and interpretations. RESULTS: Findings underscored the importance of two key constructs in understanding return to work from the individual's perspective: the personal meaning of disability and return to work relevancy. Throughout the experience of getting better and returning to work participants reflected upon the impact of personal and external factors that contributed to their work disability, sought clarity of their performance capacities and examined the importance of work and the consequences of work disability within their life circumstances. CONCLUSIONS: Insights into an individual's perceptions of their impairment and the personal relevance of work can promote a better understanding of return to work behaviour. Integrating individual perceptions is essential to advancing a multidimensional approach in return to work research.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".