Determination of good pharmacovigilance reporting practices in Quebec hospital pharmacies using a modified Delphi method
Bibliographic record
Abstract
PURPOSE: Many published guidelines are available for health care providers describing the best way to manage patient's adverse drug reactions (ADRs). However, there is a lack of guidance on the best way to promote and manage ADR reporting within hospitals. The goal of this study was to develop good pharmacovigilance reporting practices (GPRPs). METHODS: This descriptive study used a modified Delphi method. The research team developed 41 statements, according to a modified Specific Measurable Attainable Realistic Timely (SMART) method and grouped them in six categories: organization (n = 12 statements), pharmacovigilance committee (n = 4), database (n = 5), training (n = 5), tools (n = 3), and quality (n = 12). The Delphi consultation (two online rounds, conducted in 2018) involved directors of pharmacy in Quebec hospitals. RESULTS: Of 30 directors of pharmacy invited to participate in the first round, 27 (90%) did so. Following this round, the wording of five statements was modified according to pre-established rules. Twenty-five (93%) of the original 27 participants responded during the second round. Of the initial 41 statements, 37 were selected (average score ≥ 7); the other four were eliminated. Of the 37 statements selected, 22 had a "must do" formulation, 12 had a "should do" formulation, and three had a "may do" formulation. CONCLUSION: Using a modified Delphi method, we established a set of GPRPs for hospital pharmacy based on 37 statements. To our knowledge, these are the first GPRPs published in the hospital pharmacy literature.
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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 | 0.079 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".