The Influence of Patient Characteristics on the Perceived Value of Inpatient Educational Experiences by Medical Trainees
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Medical education relies heavily on workplace learning where trainees are educated through their clinical experience. Few studies have explored trainees' perceptions of the educational value of these patient care experiences. The aim of this study was to identify pediatric patient characteristics that medical trainees perceive as educationally valuable. METHODS: Over 2 months, trainees on pediatric inpatient wards ranked the perceived educational value of patients under their care on a 4-point bipolar Likert scale. Three patient characteristics were examined: complex-chronic and noncomplex-chronic preexisting conditions, difficult social circumstances, and rare diseases. Patient-level predictors of cases perceived as educationally valuable (defined as scores≥3) were examined by using univariate and multivariate analyses. RESULTS: A total of 325 patients were rated by 51 trainees (clinical medical students [45%], first-year residents [29%], third-year residents/fellows [26%]). Rare diseases had a higher educational value score (adjusted odds ratio 1.76, 95% confidence interval 1.08-2.88, P=.02). Complex-chronic and noncomplex-chronic preexisting conditions and difficult social circumstances did not affect the perceived educational value. CONCLUSIONS: Trainees attribute the most educational value to caring for patients with rare diseases. Although trainees' perceptions of learning do not necessarily reflect actual learning, they may influence personal interest and limit learning from an educational experience. Knowledge of trainee perceptions of educational experience therefore can direct medical educators' approaches to inpatient education.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".