What influences clinical educators’ motivation to teach? A BEME systematic review and framework synthesis based on self-determination theory: BEME Review No. 90
Bibliographic record
Abstract
BACKGROUND: Health professions learners are taught by full-time university faculty and by clinicians who teach alongside their clinical practice. This distributed healthcare education model ensures high-quality education but is at risk due to high learner demand, shortage of educators, and economic pressures. Understanding what factors influence clinical educators' motivation to teach may contribute to the model's sustainability and educator retention. The present review therefore aimed to systematically search and synthesise factors influencing clinical educators' motivation to teach. METHODS: Multiple databases, relevant journals, and the grey literature were searched for studies reporting on clinical educators' motivation to teach. Data were analysed using a framework synthesis method, based on self-determination theory's amotivation (e.g. disinterest or unachievable challenges), controlled (e.g. interest in rewards or pressure avoidance), and autonomous (e.g. personal importance and interest) concepts, and nested within a motivation from 'above' (i.e. interactions with stakeholders and societal expectations), 'within' (i.e. personal beliefs and personality dispositions), and 'below' (i.e. perception on learners' motivation and engagement) framework. RESULTS: Twenty-nine studies were included, published between 1998 and 2022, which reported on educators from diverse disciplines and settings. Educators reported autonomous over controlled motivation to teach, favouring enjoyment, connectedness, professional development, feeling valued for their teaching efforts, and altruistic reasons to teach, over being motivated by incentives and rewards. These results are presented in relation to their origin, as factors influencing motivation 'above', 'within', and 'below'. CONCLUSIONS: Results from this study have important implications for the development of contextual strategies to optimise learning/work environments and maximise autonomous reasons to teach, enhancing clinical educators' job satisfaction and retention.
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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 | 0.039 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".