Why Consider Patients’ Preferences?
Bibliographic record
Abstract
BACKGROUND: Several organizations are advocating for patients' preferences to be considered in clinical practice guideline development and implementation. However, lack of agreement on the goal and meaning of this policy curtails evaluation and development of patient involvement programs. GOAL: To describe guideline developers' discourses on the goal of considering patients' preferences. DESIGN: Qualitative study using discourse analysis. SUBJECTS: 18 participants (patients, health professionals, and public health experts) from 2 groups of British guideline developers. DATA COLLECTION AND ANALYSIS: Template analysis of semi-structured individual interviews was strengthened by active search for deviant cases, team debriefing, and member checking. RESULTS: All respondents supported the idea of taking account of patients' preferences in guidelines. Divergences with the goal and meaning of considering preferences were structured in 4 discourses: (1) The Governance discourse constructs guideline development as a rational process of synthesizing population data-including evidence on patients' preferences-to maximize public health within the constraints of available resources; (2) the Informed Decision discourse aims at fostering patients' choice by providing tailored information on the risks and benefits of interventions; (3) the Professional Care discourse insists on basing professionals' recommendations on the individual characteristics of patients; (4) The Consumer Advocacy discourse argues for greater political power and influence over guideline development and clinical decision making. CONCLUSIONS: The identified discourses provide a set of hypothesis on how patient involvement programs are expected to work, which could help clarify the goals pursued by guideline organizations and anchor further evaluation efforts.
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".