Implementing an evidence-informed faculty development program.
Bibliographic record
Abstract
OBJECTIVE: To establish an evidence-informed faculty development program. DESIGN: Survey derived from a needs-assessment tool. SETTING: Department of Academic Family Medicine at the University of Saskatchewan, which is geographically dispersed across the province. PARTICIPANTS: Full-time faculty members in the Department of Academic Family Medicine at the University of Saskatchewan. MAIN OUTCOME MEASURES: Creation of an evidence-informed faculty development program. RESULTS: The response rate was 77.3% (17 of 22). The data were stratified by 2 groups: faculty members with less than 5 years of experience and those with 5 or more years of experience. Those with less than 5 years of experience rated the following as their top priorities: teaching, developing scholarly activities, and career development. Those with 5 or more years of experience rated the following as their top priorities: administration and leadership, teaching, and information technology. Although there were differences in overall priorities, the 2 groups identified 17 out of 54 skills as important to faculty development. CONCLUSION: The results of the needs-assessment tool were used to shape a dynamic, evidence-informed faculty development program with full-time faculty in the Department of Academic Family Medicine at the University of Saskatchewan. Future programs will continue to be dynamic, faculty-centred, and evidence-informed.
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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 | 0.083 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".