REalist COllaborative eVAluation of a work disability prevention programme for breast cancer survivors: Protocol of the RECOVA-FASTRACS realist evaluation (Preprint)
Bibliographic record
Abstract
Background: Women with breast cancer face many barriers to returning to work (RTW) after their treatment. The Facilitating and Sustaining the Return to Work After Breast Cancer (FASTRACS) intervention aims to facilitate and sustain functional RTW. Objective: The main objective of the RECOVA-FASTRACS (Realist Collaborative Evaluation of a Work Disability Prevention Program for Breast Cancer Survivors) study is to evaluate the processes using a realist approach that analyzes what works, how, for whom, and under what circumstances. Methods: The RECOVA-FASTRACS study uses a mixed methods design to assess the implementation, context, and impact mechanisms of the FASTRACS intervention. The qualitative analysis will include 2 main components: a trajectory analysis and a focus group assessment. The trajectory analysis will examine the experiences of women who participated in the intervention and key individuals involved in their RTW process. We will use semistructured interviews according to the multiple-case study method. Additionally, to explore organizational and professional practices, focus groups will be conducted with professionals who deliver the intervention. To analyze the trajectories, embedded and iterative integration will combine the qualitative findings with relevant quantitative data from the FASTRACS randomized controlled trial for 5 domains: personal situation, professional situation, RTW, care pathway, intervention tool use, and perceived usefulness. Results: The RECOVA-FASTRACS study received funding in 2022. Recruitment and qualitative data collection began in month 6. Final analyses are expected to be completed by the end of 2026, with dissemination of the main findings anticipated in late 2027. Conclusions: Our mixed methods realist evaluation will provide a detailed analysis of the intervention processes, helping to identify impact mechanisms within specific contexts. This approach is meant to ensure a more informed and realist deployment of the intervention by professionals following the study.
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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 | 0.060 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.074 | 0.011 |
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".