Development and user testing of a patient decision aid for cancer patients considering treatment for anxiety or depression
Bibliographic record
Abstract
BACKGROUND: Despite high rates of mental health disorders among cancer patients, uptake of referral to psycho-oncology services remains low. This study aims to develop and seek clinician and patient feedback on a patient decision aid (PDA) for cancer patients making decisions about treatment for anxiety and/or depression. METHODS: Development was informed by the International Patient Decision Aid Standards and the Ottawa Decision Support Framework. Psycho-oncology professionals provided feedback on the clinical accuracy, acceptability, and usability of a prototype PDA. Cognitive interviews with 21 cancer patients/survivors assessed comprehensibility, acceptability, and usefulness. Interviews were thematically analysed using Framework Analysis. RESULTS: Clinicians and patients strongly endorsed the PDA. Clinicians suggested minor amendments to improve clarity and increase engagement. Patient feedback focused on clarifying the purpose of the PDA and improving the clarity of the values clarification exercises (VCEs). CONCLUSIONS: The PDA, the first of its kind for psycho-oncology, was acceptable to clinicians and patients. Valuable feedback was obtained for the revision of the PDA and VCEs.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".