Exploring approaches to identify, incorporate and report patient preferences in clinical guidelines: Qualitative interviews with guideline developers
Bibliographic record
Abstract
BACKGROUND: Clinical guidelines informed by patient preferences are more likely to be used and widely advocated, yet research shows that few guidelines reflect patient preferences. OBJECTIVE: Explore how developers generate guidelines informed by patient preferences. PATIENT INVOLVEMENT: Seventeen patients were involved as interview participants. METHODS: Using a basic descriptive approach, we conducted and analyzed semi-structured telephone interviews with 50 participants who were involved in developing guidelines on various topics. The sample included 17 patients, 16 clinicians and 17 managers from a total of 7 countries. RESULTS: Participants used one or more approaches to identify preferences, patient panelists, focus groups, surveys and review of published research, despite acknowledging they identified similar preferences. Participants said they incorporated preferences in all guideline development steps, but provided little detail of specific processes. Few participants said their guidelines explicitly reported how patients were engaged, preferences identified, or how preferences influenced development processes or the guideline. Enablers were patient and clinician training, supportive coordinators and chairs, involving experienced patients, and assistance from qualitative and review experts. Barriers were finding and preparing patients, clinician skepticism about benefits, and token patient involvement. Participants recommended research on how to generate preference-informed guidelines. DISCUSSION: Ideal approaches to identify, incorporate and report patient preferences in guidelines are unclear and unproven. PRACTICAL VALUE: Findings revealed specific ways that developers can enhance their processes (e.g. patient training, supportive coordinators and chairs, involve experts in qualitative researcher and systematic reviews) and key issues that warrant ongoing research (e.g. how best to incorporate and report preferences).
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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 | 0.087 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".