Qualitative study of guideline panelists: innovative surveys provided valuable insights regarding patient values and preferences
Bibliographic record
Abstract
OBJECTIVES: To explore guideline panelists' understanding of panel surveys for eliciting panels' inferences regarding patient values and preferences, and the influence of the surveys on making recommendations. STUDY DESIGN AND SETTING: We performed sampling and data collection from all four guideline panels that had conducted the surveys through October 2020. We collected the records of all panel meetings and interviewed some panelists in different roles. We applied inductive thematic analysis for analyzing and interpreting data. RESULTS: We enrolled four guideline panels with 99 panelists in total and interviewed 25 of them. Most panelists found the survey was easy to follow and facilitated the incorporation of patient values and preferences in the tradeoffs between benefits and harms or burdens. The variation of patient preferences and uncertainty regarding patient values and preferences reflected in the surveys helped the panels ponder the strength of recommendations. In doing so, the survey results enhanced a rationale for panels' decision on the recommendations. CONCLUSION: The panel surveys have proved to help guideline panels explicitly consider and incorporate patient values and preferences in making recommendations. Guideline panels would benefit from widespread use of the panel surveys, particularly when primary evidence regarding patient values and preferences is scarce.
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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 | 0.052 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".