Interventions pour le retour et le maintien au travail après un cancer : revue de la littérature
Bibliographic record
Abstract
Returning to work after cancer can be challenging for cancer survivors and little is known about interventions designed to support survivors returning to work. PURPOSE: The objective of this review was to identify interventions designed to support the return-to-work process after a cancer diagnosis. METHODS: A literature review was performed mainly done by consulting bibliographical databases. Systematic analysis and interpretation of the results were then performed. RESULTS: Twenty-two articles were identified. The first finding is that very few interventions are specifically devoted to return to work after cancer and are usually administered in the clinical setting by healthcare practitioners. The activities proposed to support return to work in these interventions are individual counselling, provision of information and support groups. These activities are provided by various multidisciplinary teams composed of one or more professionals: occupational physicians, social workers and nurses. A second finding is that even with the use of experimental and quasi-experimental approaches, no effect was observed on return to work. CONCLUSION: This integrative review highlights two recommendations for the development of future interventions. First, to improve the efficacy of future interventions on return to work of cancer survivors, these interventions must be developed and supported by an intervention theory. Second, future interventions must include and mobilize workplaces.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".