Recruitment and Retention of Community Preceptors
Bibliographic record
Abstract
BACKGROUND: Recruiting and retaining community-based pediatricians for teaching medical students has been explored through the lens of preceptors and educational leaders. The purpose of this study was to explore the perspective of pediatric department chairs, a key stakeholder group charged with maintaining teaching capacity among a faculty. METHODS: In 2015, members of the Association of Medical School Pediatric Department Chairs and Council on Medical Student Education in Pediatrics joint task force disseminated a 20-item survey to pediatric department chairs in the United States and Canada. Topics included demographics, incentives offered to community pediatricians, and the perceived value and feasibility of such incentives. Data were analyzed using descriptive statistics and χ2 to compare categorical variables. RESULTS: Pediatric department chairs from 92 of 145 (63% response rate) medical schools returned the survey. Sixty-seven percent reported difficulty recruiting or retaining preceptors, and 51% reported high-reliance on preceptors for the ambulatory portion of the pediatrics clerkship. Almost all (92%) cited competition from other programs for the services of community preceptors. The provision of incentives was correlated with perceived feasibility (R2 = 0.65) but not their perceived value (R2 = 0.12). Few (21%) chairs reported providing financial compensation to preceptors. The provision of compensation was not related to reliance but did vary significantly by geographical region (P < .001). CONCLUSIONS: Pediatric departments rely heavily on community-based pediatricians but face competition from internal and external training programs. The perspective of department chairs is valuable in weighing interventions to facilitate continued recruitment and retention of community preceptors.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".