Social networks and alcohol consumption among first generation Chinese and Korean immigrants in the Los Angeles metropolitan area
Bibliographic record
Abstract
Hofstetter, C., Clapp, J., Allem, J., Hughes, S., Li, Y., Irvin, V., Daly, A., Kang, S., & Hovell, M. (2014). Social networks and alcohol consumption among first generation Chinese and Korean immigrants in the Los Angeles metropolitan area. The International Journal Of Alcohol And Drug Research, 3(4), 245-255. doi:http://dx.doi.org/10.7895/ijadr.v3i4.188Aims: To test hypotheses involving mechanisms of reinforcement of alcohol behaviors operating in social networks.Design: Telephone interviews conducted by professional interviewers in Mandarin or Korean or English with first generation Chinese (from Mainland or Taiwan) and Korean immigrants residing using a dual frame stratified sampling design. Combined probability and non-probability approaches for sampling due to the widespread use of cell phones. Interviews were conducted in language of preferences with over 95% of interviews in Korean or Mandarin.Setting: Residents of three counties with the largest proportions of eligible residents (Los Angeles, Orange, and San Bernardino) were included.Participants: Adult residents (21 and over) stratified by gender who could be reached by telephone constituted the sample.Measures: Measures included frequency/amount alcohol consumption drawn from NIAAA standard, a “relax, socialize, have fun with” name generator was used to identify alters. Reinforcers within networks were measured by participant reports of amount of alter drinking, drunkenness, and encouragement to drink, acculturation, and demographic variables were measured by self report.Findings: Using a random effects approach and controlling for other variables, including drinking in the network, acculturation, Korean/Chinese origin, and demographics, source of immigration, network context, as was and sampling frame, encouragement to drink in the network was related to drinking (P<.05).Conclusions: Studies of social networks in relation to health behaviors should include measures of actions within networks, especially reinforcers of behaviors, in order to understand the functioning and consequences of networks.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".