Development of a generic decision guide for patients in oncology: a qualitative interview study
Bibliographic record
Abstract
Abstract Background Many patients with cancer want to be involved in healthcare decisions. For adequate participation, awareness of one’s own desires and preferences and sufficient knowledge about medical measures are indispensable. In order to support patient participation, a decision guide for patients with cancer was developed as part of a larger project called TARGET, which specifically aims to improve the care of patients with rare cancer. Methods The development of the decision guide took place from 08.2022 to 03.2023. The decision guide is a single component of a complex intervention that aims to facilitate decision support in cancer care for patients. For the development, existing development and evaluation studies of Question Prompt Lists (QPLs) were identified through systematic literature searches in the MEDLINE via PubMed, PsycInfo, and CINAHL databases. The decision guide was pre-tested for feasibility, usability, completeness and acceptance with the target groups through guided individual interviews. Sociodemographic data were collected anonymously. An expert review was conducted. The verbatim transcribed interviews were analysed using content analysis according to Kuckartz with MAXQDA. The guide has been iteratively optimized based on the results. Results A generic decision guide for patients with cancer for diagnostic or treatment decisions was developed in both PDF web-based formats, based on the Ottawa Personal Decision Guide. It was supplemented with decision-related questions from QPLs for patients with cancer. The pre-test comprised seven expert reviews of (psych)oncologists and experts in evidence-based health information and ten interviews with cancer patients (n = 7), family relatives (n = 2), and one caregiver. The results were coded into nine main categories. The results indicated a good feasibility, usability and acceptability of the guide. The tool was perceived as comprehensive and appropriate. Individual elements were identified as modifiable for better comprehensibility. The target audience appreciated the decision guide as a good support option. Conclusion The decision guide is potentially a useful support option for patients with cancer facing medical decisions in their further course of treatment. In the TARGET project, it will be made available to patients and can be supplemented with decision coaching. Further steps for implementation into healthcare structures are necessary. Clinical trial number Not applicable.
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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 | 0.051 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".