Use of social network analysis in health research: a scoping review protocol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Social networks can affect health beliefs, behaviours and outcomes through various mechanisms, including social support, social influence and information diffusion. Social network analysis (SNA), an approach which emerged from the relational perspective in social theory, has been increasingly used in health research. This paper outlines the protocol for a scoping review of literature that uses social network analytical tools to examine the effects of social connections on individual non-communicable disease and health outcomes. METHODS AND ANALYSIS: This scoping review will be guided by Arksey and O'Malley's framework for conducting scoping reviews. A search of the electronic databases, Ovid Medline, PsycINFO, EMBASE and CINAHL, will be conducted in April 2024 using terms related to SNA. Two reviewers will independently assess the titles and abstracts, then the full text, of identified studies to determine whether they meet inclusion criteria. Studies that use SNA as a tool to examine the effects of social networks on individual physical health, mental health, well-being, health behaviours, healthcare utilisation, or health-related engagement, knowledge, or trust will be included. Studies examining communicable disease prevention, transmission or outcomes will be excluded. Two reviewers will extract data from the included studies. Data will be presented in tables and figures, along with a narrative synthesis. ETHICS AND DISSEMINATION: This scoping review will synthesise data from articles published in peer-reviewed journals. The results of this review will map the ways in which SNA has been used in non-communicable disease health research. It will identify areas of health research where SNA has been heavily used and where future systematic reviews may be needed, as well as areas of opportunity where SNA remains a lesser-used method in exploring the relationship between social connections and health outcomes.
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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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it