Educating community clinicians using principles of academic detailing in an evolving landscape
Bibliographic record
Abstract
Improving prescribing practices and patient health outcomes requires not just abstract knowledge about evidence-based practices but practical tools to support implementation. Continuing education can promote clinician behavior changes and improvements in patient outcomes more effectively when it includes opportunities for interactivity, use of multiple modalities, repeated exposure to content, longer-duration activities, and emphasis of outcomes that resonate with the target audience.1 Academic detailing involves direct educational outreach,2 including face-to-face interaction with clinicians, to achieve these goals. The educational method incorporates the principles of adult learning theories,3 theory of planned behavior,4 and social marketing5,6 with the goal of improving evidence-based practice behaviors.2,7 The intervention is delivered by trained professionals, typically pharmacists, physicians, nurses, or public health workers. Academic detailing was originally adapted from the communication approach employed by pharmaceutical representatives, which is centered around brief, one-to-one interactions in a clinician’s office. Academic detailing can address situations where there is an opportunity to change clinician behavior with focused and practical educational content. In the area of prescribing, these situations include those in which medications are misused, safer or less expensive medications are underused, or therapies offering marginal benefit are overused.2 Academic detailing involves an interactive discussion with clinicians in their practice setting, with a focus on improving individual clinical performance and recommending practice changes. The individualized focus distinguishes academic detailing from other quality improvement strategies, such as general lecture-based education on a topic, care coordination, practice facilitation, and practice redesign.8
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".