Technology Use to Deliver Faculty Development: A CERA Study
Bibliographic record
Abstract
INTRODUCTION: Technology provides a platform to help address individualized training needs for community preceptors who are separated from the campus and pressured to achieve clinical productivity goals. This study explores technology use and support for delivering faculty development to community preceptors. METHODS: This cross-sectional study was part of the 2017 Council of Academic Family Medicine's (CAFM) Educational Research Alliance (CERA) annual survey of family medicine clerkship directors in the United States and Canada. RESULTS: The majority of respondents (n=62, 68.9%) agreed or strongly agreed that "using technology is critical to the successful delivery of faculty development to community preceptors." Only one-third (n=31) agreed or strongly agreed that their institution offers them adequate support to create and deliver technology-mediated faculty development or offers adequate support to community preceptors for accessing and using technology. CONCLUSIONS: Clerkship directors need institutional support to provide effective faculty development to preceptors via technology. The opportunity exists for institutions, national organizations, and professions to collaborate across disciplines and health professions on technology-based faculty development to support a level of quality and engagement for faculty development that is consistent with the levels we bring to student 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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".