A `combined framework’ approach to developing a patient decision aid: the PANDAs model
Bibliographic record
Abstract
BACKGROUND: There is a lack of practical research frameworks to guide the development of patient decision aids [PtDAs]. This paper described how a PtDA was developed using the International Patient Decision Aids (IPDAS) guideline and UK Medical Research Council (UKMRC) frameworks to support patients when making treatment decisions in type 2 diabetes mellitus. METHODS: This study used mixed methods to develop a PtDA for use in a UK general practice setting. A 10-member expert panel was convened to guide development and patients and clinicians were also interviewed individually using semi-structured interview guides to identify their decisional needs. Current literature was reviewed systematically to determine the best available evidence. The Ottawa Decision Support Framework was used to guide the presentation of the information and value clarification exercise. An iterative draft-review-revise process by the research team and review panel was conducted until the PtDA reached content and format 'saturation'. The PtDA was then pilot-tested by users in actual consultations to assess its acceptability and feasibility. The IPDAS and UKMRC frameworks were used throughout to inform the development process. RESULTS: The PANDAs PtDA was developed systematically and iteratively. Patients and clinicians highlighted the needs for information, decisional, emotional and social support, which were incorporated into the PtDA. The literature review identified gaps in high quality evidence and variations in patient outcome reporting. The PtDA comprised five components: background of the treatment options; pros and cons of each treatment option; value clarification exercise; support needs; and readiness to decide. CONCLUSIONS: This study has demonstrated the feasibility of combining the IPDAS and the UKMRC frameworks for the development and evaluation of a PtDA. Future studies should test this model for developing PtDAs across different decisions and healthcare contexts.
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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 | 0.086 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".