Studying social accountability in the context of health system strengthening: innovations and considerations for future work
Bibliographic record
Abstract
There is a growing body of research on the role of social accountability in bringing about more accessible and better-quality healthcare. Here, we refer to social accountability as "citizens' efforts at ongoing meaningful collective engagement with public institutions for accountability in the provision of public goods" (Joshi, World Dev 99:160-172, 2017). These processes have multiple interrelated components and sub-processes and engage a range of actors in community-driven, often unpredictable and context-dependent actions, which pose many methodological challenges for researchers. In June 2017, scientists and implementers working in this area came together to share experiences, discuss approaches, identify research gaps and consider directions for future studies. This paper shares learnings from this discussion.In particular, participants considered (1) how best to define and measure the complex processual nature of social accountability; (2) the study of social accountability as an inherently political process; and (3) the challenges of generalising unpredictable, community-driven and context-dependent processes. Key among a range of consensus areas was the need for researchers to capture a broader range of outcomes and better understand the nuances of implementation processes in order to effectively test theories and assumptions. Furthermore, power relationships are inherent in social accountability and the research process itself. In presenting details on these deliberations, we hope to prompt a wider discussion on the study of social accountability in health programming.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".