Bibliographic record
Abstract
Returning to work is recognized as a key aspect of cancer survivorship and returning to normality posttreatment.However, returning to work can present a number of challenges.WorkPlan is a workbook based intervention to support cancer survivors in the return to work process.This study aimed to explore the engagement and experiences of cancer survivors participating in a workbook based intervention (WorkPlan) to support return to work.Sixty-seven cancer survivors were recruited and randomized into either the intervention or the usual care arm of a feasibility RCT trial.Qualitative interviews (n = 23) were conducted with participants in the intervention arm at one month post-intervention and again at 12 months.Interviews at both times explored how participants managed their return-to-work and how engagement with the intervention provided supported that process.Interviews were audio recorded, transcribed verbatim and analyzed using a Framework approach.A total of 23 participants were interviewed.Results indicate that the workbook supported participants to create a return to work plan and communicate with their employer; participants cited this as crucial to managing their return-to-work.In addition, the workbook format of the intervention was well received with participants and they suggested ways in which hard copy materials and the act of writing were preferable to online interventions.The present study demonstrates how a workbook based intervention can support cancer survivors to successfully cope with a return to work following cancer treatment.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".