The patient engagement evaluation tool was valid for clinical practice guideline development
Bibliographic record
Abstract
Objective To evaluate reliability and validity of the six and 12 item Patient Engagement Evaluation Tool (PEET) to inform guideline developers about the quality of patient and public involvement activities. Study Design and Setting PEET-12 and three embedded validation questions were completed by patients and members of the public who participated in developing 10 guidelines between 2018 and 2020. Confirmatory factor analysis (CFA) was used to assess the validity of a single-dimension factor structure. Cronbach's alpha and Pearson correlations were calculated for internal consistency reliability. Concurrent validation was used to test the construct validity. Results A total of 290 participants completed the PEET-12. To improve tool efficiency, based on results indicating redundancy from initial item analysis and experts' review, six of 12 items were included in the final tool (PEET-6). For the PEET-6, CFA supported the single-factor structure (χ 2 (15) = 5173.4, P < 0.001, Tucker-Lewis Index = 1.00, Comparative Fit Index = 0.99, Root Mean Square Error of Approximation = 0.08). Correlation between the total score for the 3 validation questions and the PEET-6 total score was 0.71, 95% CI [0.65, 0.77], supporting construct validity. Conclusion PEET-6 and 12 are valid tools to measure patient and public involvement within settings of clinical practice guideline development.
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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 | 0.053 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".