From criteria to impact: The ASPIRE framework as a roadmap for faculty development excellence in health professions education
Bibliographic record
Abstract
Faculty development initiatives have grown globally and have become a key part of the best practices in health professions and education institutions. Systematic activities to support the roles of teachers in teaching, education, research and scholarship, and leadership and management are subject for consistent and sustainable improvement and development in each institution. The individual and organizational impacts should be envisioned from the beginning and be aligned with the institutional visions and missions. This paper describes the ASPIRE faculty development criteria as a framework of 5 crucial elements for faculty development: (1) Clear goals, systematic curriculum development model and focus on improvement; (2) Inclusive and accessible learning opportunities creating a Community of Practice (CoP); (3) Resourcing, expertise and expansion of capacity; (4) Continuous and systematic evaluation and ongoing improvement; and (5) Promoting educational innovation and scholarship in faculty development. Descriptions of elements with examples to illustrate components are provided.
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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 | 0.002 | 0.013 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".