Comparison of horizontal and traditional block family medicine curricula
Bibliographic record
Abstract
OBJECTIVE: Longitudinal curriculum model outcomes in postgraduate medical education are not well documented. The authors aimed to compare satisfaction, learning, clinical exposure, and practice intentions between longitudinal family medicine (FM) curricula and traditional rotational ("block") curricula. DESIGN: This curriculum structure evaluation used a retrospective quasi-experimental study design using data from the College of Family Physicians of Canada Family Medicine Longitudinal Survey. SETTING: The project used data from 3 FM residency programs for the entry years 2014 to 2017. PARTICIPANTS: A total of 1283 residents across 49 teaching sites were invited to participate at program entry (T1) and residency completion (T2). MAIN OUTCOME MEASURES: Data were categorized by horizontal curriculum or block curriculum. The authors used the Kirkpatrick taxonomy to compare satisfaction with the curriculum, learning, behaviour (ie, clinical exposure), and results (practice intention). One-way analyses of variance (ANOVAs) tested the effect of curriculum model on satisfaction and clinical exposure. Analyses of covariance (ANCOVAs) tested the effect of curriculum model on the other outcomes. RESULTS: =.02). The curriculum structure had no significant impact on learning and on most items in the clinical exposure or practice intention categories. CONCLUSION: Longitudinal curriculum models in residency might be associated with better resident experience. However, curriculum models do not have a significant impact on most educational outcomes, and residents from all curriculum models feel similarly prepared for practice.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".