What Is Implementation Science and What Forces Are Driving a Change in Medical Education?
Bibliographic record
Abstract
Evidence-based interventions to improve health care and medical education face multiple complex barriers to adoption and success. Implementation science focuses on the period following research dissemination, which is necessary but insufficient to address important gaps in clinician performance and patient outcomes. This article describes the forces on health care institutions, medical schools, physician clinicians, and trainees that have created the imperative to design educational interventions to address the gap between evidence and practice. These forces include accreditation, certification, licensure, and regulatory and research funding initiatives focused on improving the quality of health professions education and clinical practice. Medical educators must expand their focus on "what to change" to include "how to change" in order to prepare health care professionals and institutions to effectively adopt new evidence-based practices to improve patient, and ultimately population, outcomes.
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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 | 0.312 | 0.444 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.036 | 0.043 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".