How can clinician-educator training programs be optimized to match clinician motivations and concerns?
Bibliographic record
Abstract
BACKGROUND: Several medical schools have implemented programs aimed at supporting clinician-educators with formal mentoring, training, and experience in undergraduate medical teaching. However, consensus program design has yet to be established, and the effectiveness of these programs in terms of producing quality clinician-educator teaching remains unclear. The goal of this study was to review the literature to identify motivations and perceived barriers to clinician-educators, which in turn will improve clinician-educator training programs to better align with clinician-educator needs and concerns. METHODS: Review of medical education literature using the terms "attitudes", "motivations", "physicians", "teaching", and "undergraduate medical education" resulted in identification of key themes revealing the primary motivations and barriers involved in physicians teaching undergraduate medical students. RESULTS: A synthesis of articles revealed that physicians are primarily motivated to teach undergraduate students for intrinsic reasons. To a lesser extent, physicians are motivated to teach for extrinsic reasons, such as rewards or recognition. The key barriers deterring physicians from teaching medical students included: decreased productivity, lack of compensation, increased length of the working day, patient concerns/ethical issues, and lack of confidence in their own ability. CONCLUSION: Our findings suggest that optimization of clinician-educator training programs should address, amongst other factors, time management concerns, appropriate academic recognition for teaching service, and confidence in teaching ability. Addressing these issues may increase the retention of clinicians who are active and proficient in medical education.
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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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".