Currencies of recognition: What rewards and recognition do Canadian distributed medical education preceptors value?
Bibliographic record
Abstract
Background : Medical schools spend considerable time, effort, and money on recognition initiatives for rural and distributed medical education (DME) faculty. Previous literature has focused on intrinsic motivation to teach and there is little in the literature to guide institutional recognition efforts or to predict which items or types of recognition will be most appreciated. Methods: To better understand how rural and DME faculty in Canada value different forms of recognition, we asked faculty members from all Canadian medical schools to complete a bilingual, national online survey evaluating their perceptions of currently offered rewards and recognition. The survey received a robust response in both English and French, across nine Canadian provinces and one territory. Results: Our results indicated that there were three distinct ways that preceptors looked at recognition; these perspectives were consistent across geographic and demographic variables. These “clusters” or “currencies of recognition” included: i) Formal institutional recognition, ii) connections, growth and development, and iii) tokens of gratitude. Financial recognition was also found to be important but separate from the three clusters. Some preceptors did value support of intrinsic motivation most important, and for others extrinsic motivators, or a mix of both was most valued. Conclusions: Study results will help medical schools make effective choices in efforts to find impactful ways to recognize rural and DME faculty.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".