Creating a return to work Medical Readers’ Theatre
Bibliographic record
Abstract
BACKGROUND: Previous work on sickness absence has shown that conversations about return to work can be challenging. The perception of competing interests and multiple stakeholders in the return to work process may also complicate and erode trust, further impacting health and well-being. AIMS: This study aims to explore the themes arising from the experiences of physicians and patients on the impact of health and return to work. The goal was to use these results to develop a Medical Readers' Theatre workshop focusing on negotiating challenging return to work scenarios to serve as an educational support for stakeholders. METHODS: Semi-structured interviews were conducted with 19 physicians and 15 patients from the Canadian Maritime Provinces on their experiences in return to work following an injury or illness. Interviews were recorded, transcribed and thematically analysed. Using the emergent themes, an educational workshop in the modality of Readers' Theatre was developed. RESULTS: The findings confirm there are multiple stakeholders involved in the return to work process and the factors influencing successful return are not always medically related. Six recurring themes were identified for the patient group and five for the physicians', allowing the development of storylines and four return to work scenarios. The scenarios have been used in teaching sessions. CONCLUSIONS: The themes reinforced that challenges in return to work are not always medical in nature. This Readers' Theatre adopts perspectives of patients, physicians and other stakeholders whilst focusing on return to work with the goal of providing engagement in reflective and purposeful discussion.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".