Learning in Practice: A Valuation of Context in Time-Variable Medical Training
Bibliographic record
Abstract
The logical consequence of implementing competency-based education is moving to time-variable training. Competency-based, time-variable training (CBTVT) requires an understanding of how learners interact with their learning context and how that leads to competence. In this article, the authors discuss this relationship. They first explain that the time required to achieve competence in clinical practice depends on the availability of clinical experiences that are conducive to ongoing competence development. This requires both curricular flexibility in light of the differences in individual learners' development and a balance between longitudinal placements and transitions to different environments.Along with the deliberate use of the opportunities that learning environments offer, there is value for learners in spending ample time-in-context. For instance, guided independence is possible when trainees do not progress immediately after meeting curricular learning objectives. Next, the potential implications of CBTVT can be illustrated by two learning perspectives-Sfard's acquisition and participation metaphors-which leads to the assertion that competence is both an individual characteristic and a quality that emerges from a purposeful social interaction between individuals and their context. This theory recognizes that the deliberate use of context could be used to approach learning as acquiring collective competence.Based on this relationship between learner, context, and competence, the authors propose an approach to CBTVT that recognizes that all learners will have to meet a number of standard preset learning targets in their workplace, while still having room for further context-specific competence development and personal growth within strategically organized learning environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".