The effect of an educational intervention on meperidine use in Nova Scotia, Canada: a time series analysis
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of a prescriber focused individual educational and audit-feedback intervention undertaken by the Nova Scotia Prescription Monitoring Program (NSPMP) in March/April 2007 to reduce meperidine use. METHOD: The NSPMP records all prescriptions for controlled substances dispensed in community pharmacies in Nova Scotia, Canada. Oral meperidine use from 1 July 2005 to 31 December 2009 was examined using NSPMP data. Monthly totals for the following were obtained: number of individual patients who filled at least one meperidine prescription, number of prescriptions, and number of tablets dispensed. Data were analyzed graphically to observe overall trends. The intervention effect was estimated on the logarithmic scale with autocorrelations over time modeled by an integrated autoregressive moving average model for each outcome measure. RESULTS: An overall trend toward decreasing use from July 2005 to December 2009 was apparent for all three outcome measures. The intervention was associated with a statistically significant reduction in meperidine use, after adjusting for the overall long-term trend. Compared with the pre-intervention period, the monthly number of patients declined by 12% (p < 0.001; 95% confidence interval [CI] = 5%-18%), prescriptions by 10% (p < 0.001; 95%CI = 3%-17%), and tablets by 13.5% (p < 0.001, 95%CI = 6%-29%) in the post-intervention period. CONCLUSION: Given the risks associated with meperidine, determining that this intervention successfully reduced meperidine use is encouraging. This study highlights the potential for using population data such as the NSPMP to evaluate the effectiveness of population-level interventions to improve medication use, including professional, organizational, financial, and regulatory initiatives.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".