Lack of consistency in measurement methods and semantics used for network measures in adolescent health behaviour studies using social network analysis: a systematic review
Bibliographic record
Abstract
BACKGROUND: Social network analysis (SNA) is often used to examine how social relationships influence adolescent health behaviours, but no study has documented the range of network measures used to do so. We aimed to identify network measures used in studies on adolescent health behaviours. METHODS: We conducted a systematic review to identify network measures in studies investigating adolescent health behaviours with SNA. Measures were grouped into eight categories based on network concepts commonly described in the literature: popularity, position within the network, network density, similarity, nature of relationships, peer behaviours, social norms, and selection and influence mechanisms. Different subcategories were further identified. We detailed all distinct measures and the labels used to name them in included articles. RESULTS: Out of 6686 articles screened, 201 were included. The categories most frequently investigated were peer behaviours (n=201, 100%), position within the network (n=144, 71.6%) and popularity (n=110, 54.7%). The number of measurement methods varied from 1 for 'similarity on popularity' (within the 'similarity' category) to 28 for the 'characterisation of the relationship between the respondent and nominated peers' (within the 'nature of the relationships' category). Using the examples of 'social isolation', 'group membership', 'individuals in a central position' (within the 'position within the network' category) and 'nominations of influential peers' (sub within the 'popularity' category), we illustrated the inconsistent reporting and heterogeneity in measurement methods and semantics. CONCLUSION: Robust methodological recommendations are needed to harmonise network measures in order to facilitate comparison across studies and optimise public health intervention based on SNA.
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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 | 0.189 | 0.583 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".