Evaluations of Continuing Health Provider Education Focused on Opioid Prescribing: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Continuing health provider education (HPE) is an important intervention supported by health policy to counter the opioid epidemic; knowledge regarding appropriate program design and evaluation is lacking. The authors aim to provide a comprehensive understanding of evaluations of opioid-related continuing HPE programs and their appropriateness as interventions to improve population health. METHOD: In January 2020, the authors conducted a systematic search of 7 databases, seeking studies of HPE programs on opioid analgesic prescribing and overdose prevention. Reviewers independently screened the titles and abstracts of all studies and then assessed the full texts of all studies potentially eligible for inclusion. The authors extracted a range of data using categories for evaluating complex programs: the use of theory, program purpose, inputs, activities, outputs, outcomes, and industry involvement. Results were reported in a narrative synthesis. RESULTS: Thirty-nine reports on 32 distinct HPE programs met inclusion criteria. Of these 32, 31 (97%) were U.S./Canadian programs and 28 (88%) were reported after 2010. Measurements of changes in knowledge and confidence were common. Performance outcomes were less common and typically self-reported. Most studies (n = 27 [84%]) used concerns of opioid-related harms at the population health level to justify the educational intervention, but only 5 (16%) measured patient- or population-level outcomes directly related to the educational programs. Six programs (19%) had direct or indirect opioid manufacturer involvement. CONCLUSIONS: Continuing HPE has been promoted as an important means of addressing population-level opioid-related harms by policymakers and educators, yet published evaluations of HPE programs focusing on opioid analgesics inadequately evaluate patient- or population-level outcomes. Instead, they primarily focus on self-reported performance outcomes. Conceptual models are needed to guide the development and evaluation of continuing HPE programs intended to have population health benefits.
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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 | 0.044 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".