Just-in-time faculty development: a mobile application helps clinical teachers verify and describe clinical reasoning difficulties
Bibliographic record
Abstract
BACKGROUND: Although clinical teachers can often identify struggling learners readily and reliably, they can be reluctant to act upon their impressions, resulting in failure to fail. In the absence of a clear process for identifying and remediating struggling learners, clinical teachers can be put off by the prospect of navigating the politically and personally charged waters of remediation and potential failing of students. METHODS: To address this gap, we developed a problem-solving algorithm to support clinical teachers from the identification through the remediation of learners with clinical reasoning difficulties, which have significant implications for patient care. Based on this algorithm, a mobile application (Pdx) was developed and assessed in two emergency departments at a Canadian university, from 2015 to 2016, using interpretive description as our research design. Semi-structured interviews were conducted before and after a three-month trial with the application. Interviews were analysed both deductively, using pre-determined categories, and inductively, using emerging categories. RESULTS: Twelve clinical teachers were interviewed. Their experience with the application revealed their need to first validate their impressions of difficulties in learners and to find the right words to describe them before difficulties could be addressed. The application was unanimously considered helpful regarding both these aspects, while the mobile format appeared instrumental in allowing clinical teachers to quickly access targeted information during clinical supervision. CONCLUSIONS: The value placed on verifying impressions and finding the right words to pinpoint difficulties should be further explored in endeavours that aim to address the failure to fail phenomenon. Moreover, just-in-time mobile solutions, which mirror habitual clinical practices, may be used profitably for knowledge transfer in medical education, as an alternative form of faculty development.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".