ASPIRE for excellence in curriculum development
Bibliographic record
Abstract
The objective of the ASPIRE award programme of the International Association for Health Professions Education is to go beyond traditional accreditation processes. Working in partnership with the ASPIRE Academy, the programme aims to encourage and support excellence in health professions education, in part by showcasing and exemplifying best practices. Each year ASPIRE award applications received from institutions across the globe describe their greatest achievements in a variety of areas, one of which is curriculum development, where evaluation of applications is carried out using a framework of six domains. These are described in this paper as key elements of excellence, specifically, Organisational Structure and Curriculum Management; Underlying Educational Strategy; Content Specification and Pedagogy; Teaching and Learning Methods and Environment; Assessment, Monitoring and Evaluation; Scholarship. Using examples from the content of submissions of three medical schools from very different settings that have been successful in the past few years, achievements in education processes and outcomes of institutions around the world are highlighted in ways that are relevant to their local and societal contexts.
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 | 0.053 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.058 | 0.075 |
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".