Development of the Patient and Public Involvement Questionnaire (PPIQ) for Canadian Drug Funding Committees
Bibliographic record
Abstract
Abstract Background There is limited evidence evaluating the impact of patient and public involvement in setting priorities for healthcare, specifically drug funding recommendation committees. We describe the development phases of the Patient and Public Involvement Questionnaire (PPIQ). Methods The development of the PPIQ was informed by previous work, which established nine criteria to evaluate patient and public involvement. The PPIQ was developed with a multi-method, multi-phased approach: 1) item generation and refinement (item bank creation, user feedback sessions); 2) sensibility testing (using Feinstein’s criteria and structured interviews); and 3) pilot testing with drug funding committee’s in Canada. Results In phase one of development, a bank of 846 items were derived from key informant interviews and a literature review. Guided by the nine evaluation criteria, an initial draft of the PPIQ was created using the item bank. Two user feedback sessions (n=7) resulted in further revisions of the PPIQ. In phase two, participants (n=21) completed a sensibility questionnaire with a median score 6 out of 7 on 80% of items. Interview participants (n=14) articulated the PPIQ was clear and appropriate. In phase three, response rate for pilot testing the PPIQ was 25% (n=14) and the average time for participants to complete the PPIQ was 00:19:00 minutes (SD ± 13:46). Conclusions The methods used for the development of the PPIQ including qualitative interviews, literature review, user feedback, sensibility and pilot testing have resulted in a questionnaire that may be used with committees making drug funding recommendations in Canada.
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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 | 0.054 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".