The SAFE (Social Accountability as the Framework for Engagement) for Health Institutions project
Bibliographic record
Abstract
Problem addressed Family physicians stand to benefit from assistance with the implementation of social accountability strategies. Objective of program To develop rapid evidence narratives for key social accountability topics that summarize and mobilize evidence for practical use in social accountability strategies linking front-line, “bottom-up” actions with complementary “top-down” standards from the SAFE (Social Accountability as the Framework for Engagement) for Health Institutions evaluation tool. Program description The SAFE for Health Institutions project aims to accelerate transformation toward greater social accountability in family medicine practices and in other settings where family physicians work. A social accountability evaluation tool was developed to help with this transformation and includes a framework of 253 comprehensive top-down standards. Key social accountability topics linked to these standards were identified for rapid reviews of the literature, conducted between June and November 2021, with evidence reported as narratives. These rapid evidence narratives provide practical, evidence-based context including suggestions on how to address each topic across the micro, meso, and macro levels of care, connecting bottom-up actions with corresponding considerations for top-down policies, processes, and structures. Summaries of the rapid evidence narratives are being developed as a series of articles for Canadian Family Physician, focusing on what family physicians can do in clinical practices, with interdisciplinary teams, and in other work settings to accelerate change toward adopting or advancing socially accountable strategies. Conclusion Rapid evidence narratives that summarize and mobilize evidence on key social accountability topics further the understanding of social accountability in family medicine and in other settings where family physicians work. Mapping actions across the micro, meso, and macro levels of care is a practical way to link front-line, bottom-up actions with a top-down social accountability strategy.
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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 | 0.235 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.005 | 0.044 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".