Systematic Development of Patient Decision Aids: An Update from the IPDAS Collaboration
Bibliographic record
Abstract
Background The 2013 update of the evidence informing the quality dimensions behind the International Patient Decision Aid Standards (IPDAS) offered a model process for developers of patient decision aids. Objective To summarize and update the evidence used to inform the systematic development of patient decision aids from the IPDAS Collaboration. Methods To provide further details about design and development methods, we summarized findings from a subgroup ( n = 283 patient decision aid projects) in a recent systematic review of user involvement by Vaisson et al. Using a new measure of user-centeredness (UCD-11), we then rated the degree of user-centeredness reported in 66 articles describing patient decision aid development and citing the 2013 IPDAS update on systematic development. We contacted the 66 articles’ authors to request their self-reports of UCD-11 items. Results The 283 development processes varied substantially from minimal iteration cycles to more complex processes, with multiple iterations, needs assessments, and extensive involvement of end users. We summarized minimal, medium, and maximal processes from the data. Authors of 54 of 66 articles (82%) provided self-reported UCD-11 ratings. Self-reported scores were significantly higher than reviewer ratings (reviewers: mean [SD] = 6.45 [3.10]; authors: mean [SD] = 9.62 [1.16], P < 0.001). Conclusions Decision aid developers have embraced principles of user-centered design in the development of patient decision aids while also underreporting aspects of user involvement in publications about their tools. Templates may reduce the need for extensive development, and new approaches for rapid development of aids have been proposed when a more detailed approach is not feasible. We provide empirically derived benchmark processes and a reporting checklist to support developers in more fully describing their development processes. [Box: see text]
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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 | 0.519 | 0.707 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.043 | 0.031 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.006 | 0.011 |
| 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".