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.
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 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 | 0.151 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".