An empirical study of patient participation in guideline development: exploring the potential for articulating patient knowledge in evidence‐based epistemic settings
Bibliographic record
Abstract
BACKGROUND: Patient participation on both the individual and the collective level attracts broad attention from policy makers and researchers. Participation is expected to make decision making more democratic and increase the quality of decisions, but empirical evidence for this remains wanting. OBJECTIVE: To study why problems arise in participation practice and to think critically about the consequence for future participation practices. We contribute to this discussion by looking at patient participation in guideline development. METHODS: Dutch guidelines (n = 62) were analysed using an extended version of the AGREE instrument. In addition, semi-structured interviews were conducted with actors involved in guideline development (n = 25). RESULTS: The guidelines analysed generally scored low on the item of patient participation. The interviews provided us with important information on why this is the case. Although some respondents point out the added value of participation, many report on difficulties in the participation practice. Patient experiences sit uncomfortably with the EBM structure of guideline development. Moreover, patients who develop epistemic credibility needed to participate in evidence-based guideline development lose credibility as representatives for 'true' patients. DISCUSSION AND CONCLUSIONS: We conclude that other options may increase the quality of care for patients by paying attention to their (individual) experiences. It will mean that patients are not present at every decision-making table in health care, which may produce a more elegant version of democratic patienthood; a version that neither produces tokenistic practices of direct participation nor that denies patients the chance to contribute to matters where this may be truly meaningful.
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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 | 0.077 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".