Assessing the conceptual clarity and evidence base of quality criteria/standards developed for evaluating decision aids
Bibliographic record
Abstract
CONTEXT: Promoting patient participation in treatment decision making is of increasing interest to researchers, clinicians and policy makers. Decision aids (DAs) are advocated as one way to help achieve this goal. Despite their proliferation, there has been little agreement on criteria or standards for evaluating these tools. To fill this gap, an international collaboration of researchers and others interested in the development, content and quality of DAs have worked over the past several years to develop a checklist and, based on this checklist, an instrument for determining whether any given DA meets a defined set of quality criteria. OBJECTIVE/METHODS: In this paper, we offer a framework for assessing the conceptual clarity and evidence base used to support the development of quality criteria/standards for evaluating DAs. We then apply this framework to assess the conceptual clarity and evidence base underlying the International Patient Decision Aids Standards (IPDAS) checklist criteria for one of the checklist domains: how best to present in DAs probability information to patients on treatment benefits and risks. CONCLUSION: We found that some of the central concepts underlying the presenting probabilities domain were not defined. We also found gaps in the empirical evidence and theoretical support for this domain and criteria within this domain. Finally, we offer suggestions for steps that should be undertaken for further development and refinement of quality standards for DAs in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.722 | 0.832 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.045 | 0.020 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".