Exploring the development of a framework of social accountability standards for healthcare service delivery: a qualitative multipart, multimethods process
Bibliographic record
Abstract
OBJECTIVES: Social accountability is an equity-oriented health policy strategy that requires institutions to focus on local population needs. This strategy is well established in health professional education, but there is limited understanding of its application in healthcare service delivery. Building on what is known in the education setting, this study aimed to explore the development of a framework of comprehensive, evidence-based social accountability standards for healthcare service delivery institutions. DESIGN: This qualitative, multipart, multimethods study consisted of a modified Delphi process guided by an evidence-based social accountability tool for health professional education and complementary methods including developmental evaluation and a review of select literature to capture emerging evidence and contextual relevance. SETTING: The study took place in Northern Ontario, Canada at a medical school and a tertiary, regional academic health sciences centre that are both grounded in social accountability. PARTICIPANTS: Eight expert participants from diverse, multidisciplinary backgrounds, including a patient advocate, were purposefully recruited from both institutions, enrolled and seven completed the study. MAIN OUTCOME: The resulting framework of social accountability standards is organised into 4 major sections that capture broad and critical concepts; 17 key component reflective questions that address key themes; 39 aspirations that describe objective standards and 197 indicators linked to specific expectations. RESULTS: Three modified Delphi rounds were completed producing a framework of consensus derived standards. Developmental evaluation helped identify facilitators, barriers and provided real-time feedback to the study's processes and content. The literature reviewed identified 10 new concepts and 43 amendments. CONCLUSION: This study highlights the development of a comprehensive, evidence-based framework of social accountability standards for healthcare service delivery institutions. Future studies will aim to evaluate the application of these standards to guide equity-oriented social accountability health policy strategies in healthcare service delivery.
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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 | 0.183 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| 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".