Physicians' perspectives on cancer survivors' work integration issues.
Bibliographic record
Abstract
OBJECTIVE: To explore physicians’ perspectives on supporting cancer survivors’ work integration (WI) issues. DESIGN: Using vignette methodology, 10 physicians were individually interviewed. Interviews were audiorecorded, transcribed, and subsequently analyzed. SETTING: Ontario. PARTICIPANTS: A total of 10 physicians participated: 5 oncologists and 5 FPs. METHODS: An inductive interpretive description approach was used to identify themes across the entire data set. MAIN FINDINGS: Physicians primarily focused on patients’ medical needs and did not spontaneously address WI issues with them. Instead, it was their patients who raised WI issues, most often owing to insurance requirements. Physicians readily completed insurance forms to aid patients’ well-being, but they did not believe their guidance was empirically sound based upon their limited WI training; rather, they recognized other health professionals, such as occupational therapists, as being better equipped to address cancer survivors’ WI issues. Despite this recognition, referrals for WI support were not routinely facilitated owing to a lack of resources or knowledge. CONCLUSION: Owing to a lack of training and time, as well as the belief that WI issues are not part of their mandate of care, physicians perceive themselves as ill-equipped to address cancer survivors’ WI issues. These findings underscore the need for enhanced awareness of cancer survivors’ WI issues and the need for accessible support services offered by duly trained health care professionals, such as occupational therapists, ideally working in a multidisciplinary team to holistically address cancer survivors’ unique needs.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".