Integrating socially accountable health professional education and learning health systems to transform community health and health systems
Bibliographic record
Abstract
A learning health system aims to create value in health systems using data-driven innovations, quality improvement techniques, and collaborations between health system partners. Although the concept is mobilized through cycles of learning, most instantiations of the learning health system overlook the importance of formalized learning in educational settings. Social accountability in health professional education focuses on measurably improving people's health and health care, specifically through education and training activities. In this commentary, we argue that the idea of social accountability clearly articulates a rationale and a broad range of aspirations, whereas the learning health system offers an approach to achieve these goals. With a similar aim to a learning health system, social accountability promotes partnerships between health professional education, the health system, and communities in a way that allows for co-designed and contextualized interventions. On the other hand, learning health systems prioritize data, research, and analytic capacities to facilitate quality improvement. An integrative framework could enhance learning cycles by collectively designing interventions and innovations with people and communities from health, research, and education systems. As well as aspiring to improve population health and health equity, such a framework will consider broader impacts, including the degree of participation amongst a range of partners and the level of responsiveness to partners' priorities.
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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.011 |
| 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 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".