“How is it for you?” – residents’ and faculty experience with a new family medicine competency-based curriculum
Bibliographic record
Abstract
Background/Purpose The University of Calgary Family Medicine (FM) residency program introduced a new “Triple-C”, competency based curriculum in 2012. This presented an opportunity to study in depth, the impact of such a major change on both Faculty and Residents. Methodology Semi-structured interviews were completed with 10 second-year FM residents and 16 Faculty involved in the introduction of the new curriculum. Study participants were selected using purposeful sampling method. Interviews were recorded and subsequently transcribed verbatim for thematic analysis. Results The analysis revealed a wide variation in residents’ and Faculty understanding of the elements of a “Triple-C”, competency-based curriculum. Study participants identified issues relating to the delivery of quality, consistent, and equitable learning experiences in a large residency Program. Scheduled learning experiences with non-physician health care professionals appeared to be less valued by residents than those with physicians, and significant challenges around providing experience of continuity of care were also identified, especially in larger academic teaching clinics. Conclusions For a new curriculum to be successful, an ongoing process of evaluation and monitoring of learning experiences is essential. Despite some deficiencies and implementation challenges identified by study participants, both residents and Faculty acknowledged that these were expected, and were willing to commit to and engage with the new curriculum. Understanding how the “Triple-C” curriculum impacted our learners and Faculty provided essential feedback to curriculum developers, and enhanced our ongoing processes of quality assurance and improvement within the Program.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".