Attributes of excellent clinician teachers and barriers to recognizing and rewarding clinician teachers’ performances and achievements: a narrative review
Bibliographic record
Abstract
Background: Over the last 31 years, there have been several institutional efforts to better recognize and reward clinician teachers. However, the perception of inadequate recognition and rewards by clinician teachers for their clinical teaching performance and achievements remains. The objective of this narrative review is two-fold: deepen understanding of the attributes of excellent clinician teachers considered for recognition and reward decisions and identify the barriers clinician teachers face in receiving recognition and rewards. Methods: We searched OVID Medline, Embase, Education Source and Web of Science to identify relevant papers published between 1990 and 2020. After screening for eligibility, we conducted a content analysis of the findings from 43 relevant papers to identify key trends and issues in the literature. Results: We found the majority of relevant papers from the US context, a paucity of relevant papers from the Canadian context, and a declining international focus on the attributes of excellent clinician teachers and barriers to the recognition and rewarding of clinician teachers since 2010. 'Provides feedback', 'excellent communication skills', 'good supervision', and 'organizational skills' were common cognitive attributes considered for recognition and rewards. 'Stimulates', 'passionate and enthusiastic', and 'creates supportive environment', were common non-cognitive attributes considered for recognition and rewards. The devaluation of teaching, unclear criteria, and unreliable metrics were the main barriers to the recognition and rewarding of clinician teachers. Conclusions: The findings of our narrative review highlight a need for local empirical research on recognition and reward issues to better inform local, context-specific reforms to policies and practices.
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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 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".