Medical students’ reactions to an experience-based learning model of clinical education
Bibliographic record
Abstract
An experience-based learning (ExBL) model proposes: Medical students learn in workplaces by 'supported participation'; affects are an important dimension of support; many learning outcomes are affective; supported participation influences students' professional identity development. The purpose of the study was to check how the model, which is the product of a series of earlier research studies, aligned with students' experiences, akin to the 'member checking' stage of a qualitative research project. In three group discussions, a researcher explained ExBL to 19 junior clinical students, who discussed how it corresponded with their experiences of clinical learning and were given a written précis of it to take away. One to 3 weeks later, they wrote 500-word reflective pieces relating to their subsequent experiences with ExBL. Four researchers conducted a qualitative analysis. Having found many instances of responses 'resonating' to the model, the authors systematically identified and coded respondents' 'resonances' to define how they aligned with their experiences. 120 resonances were identified. Seventy (58 %) were positive experiences and 50 (42 %) negative ones. Salient experiences were triggered by the learning environment in 115 instances (96 %) and by learners themselves in 5 instances (4 %), consistent with a strong effect of environment on learning processes. Affective support was apparent in 129 of 203 statements (64 %) of resonances and 118 learning outcomes (58 %) were also affective. ExBL aligns with medical students' experiences of clinical learning. Subject to further research, these findings suggest ExBL could be used to support the preparation of faculty and students for workplace learning.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".