Exploring Acceptability of Employment Interventions to Support People Living With Cancer: Qualitative Study of Cancer Survivors, Health Care Providers, and Employers
Bibliographic record
Abstract
BACKGROUND: Employment contributes to cancer survivors' quality of life, but this population faces a variety of challenges when working during and after treatment. Factors associated with work outcomes among cancer survivors include disease and treatment status, work environment, and social support. While effective employment interventions have been developed in other clinical contexts, existing interventions have demonstrated inconsistent effectiveness in supporting cancer survivors at work. We conducted this study as a preliminary step toward program development for employment support among survivors at a rural comprehensive cancer center. OBJECTIVE: We aimed (1) to identify supports and resources that stakeholders (cancer survivors, health care providers, and employers) suggest may help cancer survivors to maintain employment and (2) to describe stakeholders' views on the advantages and disadvantages of intervention delivery models that incorporate those supports and resources. METHODS: We conducted a descriptive study collecting qualitative data from individual interviews and focus groups. Participants included adult cancer survivors, health care providers, and employers living or working in the Vermont-New Hampshire catchment area of the Dartmouth Cancer Center in Lebanon, New Hampshire. We grouped interview participants' recommended supports and resources into 4 intervention delivery models, which ranged on a continuum from less to more intensive to deliver. We then asked focus group participants to discuss the advantages and disadvantages of each of the 4 delivery models. RESULTS: Interview participants (n=45) included 23 cancer survivors, 17 health care providers, and 5 employers. Focus group participants (n=12) included 6 cancer survivors, 4 health care providers, and 2 employers. The four delivery models were (1) provision of educational materials, (2) individual consultation with cancer survivors, (3) joint consultation with both cancer survivors and their employers, and (4) peer support or advisory groups. Each participant type acknowledged the value of providing educational materials, which could be crafted to improve accommodation-related interactions between survivors and employers. Participants saw usefulness in individual consultation but expressed concern about the costs of program delivery and potential mismatches between consultant recommendations and the limits of what employers can provide. For joint consultation, employers liked being part of the solution and the possibility of enhanced communication. Potential drawbacks included additional logistical burden and its perceived generalizability to all types of workers and workplaces. Survivors and health care providers viewed the efficiency and potency of peer support as benefits of a peer advisory group but acknowledged the sensitivity of financial topics as a possible disadvantage of addressing work challenges in a group setting. CONCLUSIONS: The 3 participant groups identified both common and unique advantages and disadvantages of the 4 delivery models, reflecting varied barriers and facilitators to their potential implementation in practice. Theory-driven strategies to address implementation barriers should play a central role in further intervention development.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".