Occupational Physicians’ Reasoning about Recommending Early Return to Work with Work Modifications
Bibliographic record
Abstract
Previous research indicates that work modifications can effectively enhance return to work (RTW) at an early stage of work disability. We aimed to examine how occupational physicians (OPs) reason about recommending early return to work (RTW) with work modifications. Pre-defined propositions regarding the use of work modifications in promoting early RTW were discussed in four focus groups with altogether 11 Finnish OPs. Discussions were audio recorded, and the transcribed data were analyzed using qualitative content analysis. Five different rationales for supporting early RTW were identified: to manage medical conditions, to enhance employee well-being, to help workplace stakeholders, to reduce costs to society, and to enhance OP's own professional fulfillment. However, OPs identified situations and conditions in which early RTW may not be suitable. In addition, there were differences between the OPs in the interpretation of the rationales, suggesting variation in clinical practice. In conclusion, encouraging early RTW with work modifications was perceived by OPs as a meaningful task and, to a large extent, beneficial for employees and several stakeholders. However, this practice was not accepted without consideration to the RTW situation and context. If early RTW and work modifications are to be promoted, OPs should be offered education that addresses their views regarding this practice.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".