A N-of-1 social network approach to study the social dynamics of alcohol consumption
Bibliographic record
Abstract
Introduction: The aim of this study was to investigate how the dynamics of the social environment impacted the alcohol consumption of individuals who self-identified as heavy drinkers. Methods: A mixed methods approach including N-of-1 study with daily Ecological Momentary Assessment (EMA) followed by a social network egonet interview. Qualitative data was analysed using deductive and inductive approaches. The main quantitative outcomes were a number of social contacts and the supportiveness of social networks. Results: Fifteen participants provided sufficient EMA data regarding social contact and six of these took part in the egonet interviews. EMA respondents reported 10.8 social contacts on average and rated approximately half of their networks as positive supports; approximately 10% of each respondents' networks were perceived as 'drinking a lot'. Interview data illustrated the influence of peer and family networks; stress; motivation levels; and coping strategies within the context of the social world. EMA and egonet methods proved feasible with this specific population demonstrating the utility of innovative approaches to study dynamic social contexts related to substance use. Discussion: Respondents either drew upon their social resources and implemented strategies to support behaviour change or experienced social strain and poor mental health in the absence of supportive social strategies. Future research should explore how social networks can impact maintaining non-drinking status and accessing supports. Mixed methods research combining N-of-1, EMA, and egonets can provide novel insights into social dynamics.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".