Is there a way for clinical teachers to assist struggling learners? A synthetic review of the literature
Bibliographic record
Abstract
Struggling medical trainees pose a challenge to clinical teachers, since these learners warrant closer supervision that is time-consuming and competes with time spent on patient care. Clinical teachers' perception that they are ill equipped to address learners' difficulties efficiently may lead to delays or even lack of remediation for these learners. Because of the paucity of evidence to guide best practices in remediation, the best approach to guide clinical teachers in the field remains to be established. We aimed to present a synthetic review of the empirical evidence and theory that may guide clinical teachers in their daily task of supervising struggling learners, reviewing current knowledge on the challenges and solutions that have been identified and explored. A computerized literature search was performed using Medline, Embase, Education Resources Information Center, and Education Source, after which final articles were selected based on relevance. The literature reviewed provided best evidence for clinical teachers to address learners' difficulties, which is presented in the order of the four steps inherent to the clinical approach: 1) detecting a problem based on a subjective impression, 2) gathering and documenting objective data, 3) assessing data to make a diagnosis, and 4) planning remediation. A synthesized classification of pedagogical diagnoses is also presented. This review provides an outline of practical recommendations regarding the supervision and management of struggling learners up to the remediation phase. Our findings suggest that future research and faculty development endeavors should aim to operationalize remediation strategies further in response to specific diagnoses, and to make these processes more accessible to clinical teachers in the field.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".