Expertise, Time, Money, Mentoring, and Reward: Systemic Barriers That Limit Education Researcher Productivity—Proceedings From the AAMC GEA Workshop
Bibliographic record
Abstract
BACKGROUND: To further evolve in an evidence-based fashion, medical education needs to develop and evaluate new practices for teaching, learning, and assessment. However, educators face barriers in designing, conducting, and publishing education research. OBJECTIVE: To explore the barriers medical educators face in formulating, conducting, and publishing high-quality medical education research, and to identify strategies for overcoming them. METHODS: A consensus workshop was held November 5, 2013, at the Association of American Medical Colleges annual meeting. A working group of education research experts and educators completed a preconference literature review focusing on barriers to education research. During the workshop, consensus-based and small group techniques were used to refine the broad themes into content categories. Attendees then ranked the most important barriers and strategies for overcoming them with the highest potential impact. RESULTS: Barriers participants faced in conducting quality education research included lack of (1) expertise, (2) time, (3) funding, (4) mentorship, and (5) reward. The strategy considered most effective in overcoming these barriers involved building communities of education researchers for collaboration and networking, and advocating for education researchers' interests. Other suggestions included trying to secure increased funding opportunities, developing mentoring programs, and encouraging mechanisms to ensure protected time. CONCLUSIONS: Barriers to education research productivity clearly exist. Many appear to result from feelings of isolation that may be overcome with systemic efforts to develop and enable communities of practice across institutions. Finally, the theme of "reward" is novel and complex and may have implications for education research productivity.
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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 | 0.067 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".