Evaluating Continuing Health Professions Education as Health Policy for the Opioid Crisis
Bibliographic record
Abstract
Continuing health professions education (CHPE) has been considered an essential intervention for addressing the Canadian opioid crisis. CHPE programs are complex – they involve the actions of people and a complex chain of steps, are embedded in social systems shaped by context, and are open systems subject to change. Drawing on frameworks for evaluating complex interventions, the objectives of this thesis were to: 1) systematically assess all published evaluative reports of CHPE programs for their appropriateness as interventions to reduce opioid-related harms and improve population health; 2) conduct an implementation evaluation of the Safer Opioid Prescribing CHPE program; and 3) develop a conceptual model to support the design and evaluation of population health focused CHPE. Addressing opioid-related harms was a consistent justification for the development and delivery of the 32 opioid analgesic prescribing CHPE programs. Yet, evaluations of these programs rarely encompassed patient- or population-level outcomes, focusing primarily on self-reported performance outcomes. There was likewise little explicit use of educational or other theory reported in program development and evaluation. Implementation evaluations can support such interrogations of program theory. It was determined that the Safer Opioid Prescribing program was implemented as intended along the outcomes of reach, dose, fidelity, and participant responsiveness. This identified a promising model for using virtual continuing health professions education to reach a critical mass of prescribers to address large scale health problems. However, due to the complex and dynamic nature of the opioid crisis, scaling up of successfully implemented programs is no guarantee achieving beneficial population health outcomes. Drawing on the fields of clinical population medicine, the social determinants of health, health equity, and philosophies of population health, at least five ways of re-orienting CHPE programs towards population health were identified. These included: 1) scaling effective CME programs while evaluating at population health levels; 2) (re)interpreting evidence for program content from a population perspective; 3) incorporating social determinants of health into clinically-oriented CME activities; 4) explicitly building fluency in population health concepts and practices among health care providers and CME planners; and 5) attending to social inequity in every aspect of CME programs.
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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 | 0.120 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".