EGOCENTRIC SOCIAL NETWORK FACTORS AND ALCOHOL MISUSE AND RECOVERY
Bibliographic record
Abstract
An individual’s alcohol use influences, and is influenced by, the drinking in their social network. Indeed, a person’s propensity to drink heavily is associated with having a social network in which more people drink heavily. This association has largely been reported in non-clinical samples of young adults and in networks of 10 or fewer relationships. Exploring social networks in older adults and in more distal relationships may further elucidate the interplay between an individual’s drinking and their social network. This dissertation explores the associations between a person’s social network and their drinking behaviour in adults, in clinical samples of people with alcohol use disorder (AUD), and in both proximal and distal social networks. The first study explores the psychometric properties of a brief egocentric (i.e., individual self-reports on their network) social network analysis (SNA) measure in general community adult drinkers. The second study compares a brief and a more extensive, or formal, SNA measure in terms of the capacity to identify AUD. The third study investigates the association between social networks and recovery from AUD, using a simple measure of social network alcohol use. Finally, the fourth study examines whether a higher-resolution SNA tool could provide a more in-depth understanding of the relationship between social networks and recovery from AUD. The results of this dissertation validate several uses of SNA in the context of AUD in adults: first, a brief measure demonstrates excellent psychometric properties. Second, both brief and formal SNA measures demonstrate the capacity to accurately classify those with and without AUD. Third, social networks play both a moderating and/or mediating role in AUD recovery, depending on the phase of recovery and type of social network measure used. Collectively, this dissertation provides an important foundation for further applications of SNA in clinical research and practice.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".