ASPIRE to excellence: Making health systems socially accountable
Bibliographic record
Abstract
This article explores the evolving landscape of social accountability in medical education through the lens of the AMEE ASPIRE-to-Excellence initiative. Social accountability has become increasingly recognized as a fundamental principle in health professional education, requiring health professional education and training programs to align their teaching, research, and service activities with the priority health concerns, namely those of the communities they serve. Drawing on the ASPIRE Social Accountability criteria, this paper outlines how these elements function not only as assessment tools but as building blocks for embedding social accountability into curricula and institutional missions. Essential elements include mission-driven leadership and governance, community co-design, equitable student recruitment, socially responsive curricula, community-engaged research, contribution to health services, impact measurement, and continuous quality improvement. The paper illustrates this through examples of excellence in different contexts and identifies key challenges and strategies for expanding social accountability in health professional education and training programs. The discussion emphasizes that social accountability is a dynamic, context-sensitive endeavor grounded in authentic partnerships, continuous quality improvement, and an inclusive approach to health, equity and diversity. The conclusion highlights the call to action: to embrace these criteria as living elements that can guide institutions in fostering socially accountable, environmentally sustainable, and technologically responsive health professionals.
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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 | 0.087 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.053 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.003 | 0.049 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".