Satisfaction, Motivation, and Future of Community Preceptors
Bibliographic record
Abstract
PURPOSE: To measure overall satisfaction of community-based preceptors, their anticipated likelihood of continuing to teach, professional satisfaction, influence of having students, motivation for teaching, satisfaction with professional practice, and satisfaction with and value of incentives, and to compare results with those of a similar 2005 statewide survey. METHOD: In 2011, the authors distributed a 25-item survey to all 2,359 community-based primary care preceptors (physicians, pharmacists, advanced practice nurses, physician assistants) served by the North Carolina Area Health Education Centers system's Offices of Regional Primary Care Education. The survey targeted the same items and pool of eligible respondents as did the North Carolina Area Health Education Center 2005 Preceptor Survey. RESULTS: Of 2,359 preceptors contacted, 1,278 (54.2%) completed questionnaires. The data from 2011 did not differ significantly from the 2005 data. In 2011, respondents were satisfied with precepting (91.7%), anticipated continuing to precept for the next five years (88.7%), and were satisfied overall with their professional life (93.7%). Intrinsic reasons (e.g., enjoyment of teaching) remained an important motivation for teaching students. Physicians reported significantly lower overall satisfaction with extrinsic incentives (e.g., monetary compensation) and felt more negativity about the influence of students on their practices. CONCLUSIONS: This study found that preceptors continue to be satisfied with teaching students. Intrinsic reasons remain an important motivation to precept, but monetary compensation may have increasing importance. Physicians responded more negatively than other health provider groups to several questions, suggesting that their needs might be better met by redesigned teaching models.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".