Returning to Work After Cancer Treatment: An Exploratory Sequential Mixed-Methods Study Guided by Transitions Theory
Bibliographic record
Abstract
BACKGROUND: Although many individuals return to work after cancer treatment, supports to facilitate this transition are ineffective or lacking. Transitions Theory can be useful to conceptually explain the transition back to work after cancer; however, no known studies have used Transitions Theory to empirically examine this transition. OBJECTIVE: To explore how and why Transition Theory concepts can be used to understand individuals' transition back to work after cancer treatment. METHODS: Using an explanatory sequential mixed-methods design, breast or colorectal cancer survivors who had returned to work completed questionnaires aligned with Transitions Theory concepts. Spearman correlations were used to explore associations, and significant results were used to draft interview questions. One-to-one telephone interviews with a subsample of participants provided elaborations to quantitative results. Qualitative data were analyzed using template analysis. RESULTS: Among the 23 participants who returned questionnaires, most identified as female (n = 21 [91%]) and had been back at work for 28.9 months (range, 3-60). The sample's productivity loss was 7.42%, indicating an incomplete mastery of their return to work. Only 2 significant associations were revealed with higher productivity loss: higher anxiety ( r = 0.487, P = .019) and a greater number of unmet relational needs ( r = 0.416, P = .048). Twelve participants engaged in interviews wherein explanations for quantitative results were uncovered. CONCLUSIONS: To support a smoother transition back to work after cancer, assessment and interventions should focus on individuals' psychological well-being and relationship needs. IMPLICATIONS FOR PRACTICE: Transitions Theory can be useful in developing interventions to support a successful return to work after cancer.
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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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".