A Digital Coaching Intervention for Cancer Survivors With Job Loss: Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Returning to work is a key unmet need for working-age cancer survivors. OBJECTIVE: This study sought to evaluate return-to-work outcomes of a multidisciplinary intervention provided as routine employee support. METHODS: In a retrospective cohort analysis, patients with cancer and more than 3 months of absence from work were provided with an intervention consisting of digital resources and calls with a health coach. Propensity score matching was used to define a similar cohort of cancer patients absent from work, who were not offered the coaching intervention. The return-to-work rate as a percentage of all participants and secondary outcomes, such as the rate of death, were measured. The median time to return to work was compared between the cohorts using the Kaplan-Meier method. RESULTS: A total of 220 participants were enrolled in the intervention, of which 125 met the criteria for analysis. The median follow-up from cancer diagnosis was 79 weeks (IQR 60-106 weeks). In the matched control group, 22 (17.6%) participants returned to work compared with 38 (30.4%) in the intervention group (P=.02). Additionally, 19 (15.2%) matched controls died prior to claim closure compared with 13 (10.4%) in the intervention group (P=.26). The Kaplan-Meier estimated median time for the first 15% of the cohort to return to work was 87.1 weeks (95% CI 60.0-109.1 weeks) for the matched control group compared with 70.6 weeks (95% CI 52.6-79.6 weeks; P=.08) for the intervention group. CONCLUSIONS: Patients receiving a remotely delivered coaching program in a real-world setting returned to work at a higher frequency than did control participants receiving usual care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".