238 Development and piloting of a generic decision guide for patients in oncology
Bibliographic record
Abstract
Introduction Patients in oncology want to be involved in healthcare decisions. To prepare patients to actively participate in decision-making, we aimed to develop and pilot test a generic decision guide (DG) for patients in oncology as part of the TARGET project funded by the German Innovation Fund. Methods The study was conducted between 08.2022 and 03.2023 using the MRC framework for the development and evaluation of complex interventions. A systematic review was performed to identify information needs and available decision support tools (DST) in MEDLINE via Pubmed, PsycInfo and CINAHL. The feasibility, user-friendliness and acceptance by the target group were tested with guided individual interviews. Experts (developer of decision support tools and psycho-oncologists) were invited to review the DG. Interview transcripts were qualitatively analyzed according to Kuckartz using the software MAXQDA®. The DG was iteratively optimized according to the results. Results Based on the Ottawa Personal Decision Guide and the information needs, a generic DG for oncology patients was developed in a PDF- and web-based format. Decision-related questions from question prompt lists for patients in oncology were added. Seven cancer patients, two medical laypersons and one caregiver were interviewed. The results showed good feasibility, usability and acceptance of the DG. The tool was perceived as detailed and appropriate. Individual elements needed to be adapted in order to improve comprehensibility. Discussion The study showed that a generic decision guide adapted to the specific needs of oncology patients was appreciated by the target group as useful and supportive, especially to facilitate values-based decision-making. It can be used as single intervention or combined with decision coaching. Conclusion(s) Further steps are needed to implement the DG into oncology care pathways.
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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 | 0.033 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".