Incentives for clinical teachers: On why their complex influences should lead us to proceed with caution
Bibliographic record
Abstract
INTRODUCTION: When medical education programs have difficulties recruiting or retaining clinical teachers, they often introduce incentives to help improve motivation. Previous research, however, has shown incentives can unfortunately have unintended consequences. When and why that is the case in the context of incentivizing clinical teachers remains unclear. The purposes of this study, therefore, were to understand what values and motivations influence teaching decisions; and to delve deeper into how teaching incentives have been perceived. METHODS: An interpretive description methodology was used to improve understanding of the development and delivery of teaching incentives. A purposeful sampling strategy identified a heterogenous sample of clinical faculty teaching in undergraduate and postgraduate contexts. Sixteen semi-structured interviews were conducted and transcripts were analyzed using an iterative process to develop a thematic structure that accounts for general trends and individual variations. RESULTS: Clinicians articulated interrelated and dynamic personal and environmental factors that had linear, dual-edged and inverted U-shaped impacts on their motivations towards teaching. Barriers were frequently rationalized away, but cumulative barriers often led to teaching attrition. Clinical teachers were motivated when they felt valued and connected to their learners, peers, leadership, and/or the medical education community. While incentives aimed at producing these connections could be perceived as supportive, they could also negatively impact motivation if they were impersonal, inequitable, inefficient, or poorly framed. DISCUSSION/CONCLUSION: These findings reinforce the literature suggesting that it is necessary to proceed with caution when labeling any particular factor as a motivator or barrier to teaching. They take us deeper, however, towards understanding how and why clinical teachers' perceptions are unique, dynamic and fluid. Incentive schemes can be beneficial for teacher recruitment and retention, but must be designed with nuance that takes into account what makes clinicians feel valued if the strategy is to do more good than harm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".