University of Ottawa Department of Family Medicine Faculty Development Curriculum Framework
Bibliographic record
Abstract
In response to the challenges faced by rapid expansion and curriculum reform, the Department of Family Medicine (DFM) at the University of Ottawa (U of O) developed a Faculty Development Conceptual Framework (FDCF) and companion plan as a first step toward meeting the challenges of providing quality opportunities for the continuing professional development of preceptors in Family Medicine. The FDCF outlines the processes, opportunities and support structures needed to improve preceptors’ teaching skills and effectively deliver a newly revised “Triple C” competency-based curriculum. The FDCF acts as a quality standard to guide the design, delivery, and evaluation of a vibrant Faculty Development (FD) Program. It further provides a structure for implementing Enterprise Resource Planning (ERP) web applications to facilitate the flow of information between seven teaching sites, provide consistency among programs, and play a tactical role in the sharing of academic resources. This chapter introduces the DFM’s FDCF so other medical departments may benefit from the authors’ experiences and adapt or adopt the framework applications and methodologies to improve the effectiveness and efficiency of FD products and processes. Modifications to the framework are expected as this program continues to evolve.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".