Integrating teaching into the busy resident schedule: A learner-centered approach to raise efficiency (L-CARE) in clinical teaching
Bibliographic record
Abstract
BACKGROUND: Clinical teachers are sometimes challenged by residents who seem too busy to concentrate on their learning. In such situations, teachers must be aware to diagnose underlying problems in learners and to effectively help them maximize learning while minimizing time and energy requirements. OBJECTIVE: To develop a learner-centered model to improve efficiency of clinical teaching. METHODS: We reviewed the literature on educational diagnosis, self-directed learning, and effective/efficient teaching to put together a new model. RESULTS: The Learner-Centered Approach to Raise Efficiency (L-CARE) in Clinical Teaching is inspired from the well-known patient-centered clinical method. Using the L-CARE in clinical teaching involves: (1) addressing the learners' feelings regarding their environment as well as patient care and study issues, which provides a good learning climate facilitating educational diagnosis and management of issues that could impair learning; (2) establishing a learning contract (expectations); (3) sharing resources and strategies (ideas) that should be effective without wasting time or energy; (4) self-assessment and constructive feedback (impact). These steps are grounded in self-directed learning theory to improve motivation and ensure that learners concentrate on their own needs to promote learning efficiency. CONCLUSION: The L-CARE model integrates educational diagnosis principles, self-directed learning theory, and efficient teaching strategies to improve efficiency of clinical teaching.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".