How to identify, incorporate and report patient preferences in clinical guidelines: A scoping review
Bibliographic record
Abstract
BACKGROUND: Clinical guidelines optimize care delivery and outcomes. Guidelines support patient engagement and adherence if they reflect patient preferences for treatment options, risks and benefits. Many guidelines do not address patient preferences. Developers require insight on how to develop such guidelines. OBJECTIVE: To conduct a scoping review on how to identify, incorporate and report patient preferences in guidelines. SEARCH: We searched MEDLINE, EMBASE, Scopus, CINAHL, OpenGrey and GreyLit from 2010 to November 2019. ELIGIBILITY: We included English language studies describing patient preferences and guidelines. DATA EXTRACTION AND SYNTHESIS: We reported approaches for and determinants and impacts of identifying patient preferences using summary statistics and text, and interpreted findings using a conceptual framework of patient engagement in guideline development. RESULTS: Sixteen studies were included: 2 consulted patients and providers about patient engagement approaches, and 14 identified patient preferences (42.9%) or methods for doing so (71.4%). Studies employed single (57.1%) or multiple (42.9%) methods for identifying preferences. Eight (57.1%) incorporated preferences in one aspect of guideline development, while 6 (42.9%) incorporated preferences in multiple ways, most commonly to identify questions, benefits or harms, and generate recommendations. Studies did not address patient engagement in many guideline development steps. Included studies were too few to establish the best approaches for identifying or incorporating preferences. Fewer than half of the studies (7, 43.8%) explored barriers. None examined reporting preferences in guidelines. CONCLUSIONS: Research is needed to establish the single or multiple approaches that result in incorporating and reporting preferences in all guideline development steps.
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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 | 0.165 | 0.513 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.042 | 0.034 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".