Implementing a collaborative return-to-work program: Lessons from a qualitative study in a large Canadian healthcare organization
Bibliographic record
Abstract
BACKGROUND: Comprehensive workplace return-to-work policies, applied with consistency, can reduce length of time out of work and the risk of long-term disability. This paper reports on the findings from a qualitative study exploring managers' and return-to-work-coordinators' views on the implementation of their organization's new return-to-work program. OBJECTIVES: To provide practical guidance to organizations in designing and implementing return-to-work programs for their employees. METHODS: Semi-structured qualitative interviews were undertaken with 20 managers and 10 return-to-work co-ordinators to describe participants' perspectives on the progress of program implementation in the first 18 months of adoption. The study was based in a large healthcare organization in Ontario, Canada. Thematic analysis of the data was conducted. RESULTS: We identified tensions evident in the early implementation phase of the organization's return-to-work program. These tensions were attributed to uncertainties concerning roles and responsibilities and to circumstances where objectives or principles appeared to be in conflict. CONCLUSIONS: The implementation of a comprehensive and collaborative return-to-work program is a complex challenge. The findings described in this paper may provide helpful guidance for organizations embarking on the development and implementation of a return-to-work program.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 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".