Community-Specific Risk and Protective Factors for Risky Alcohol Consumption in American Indian Women of Reproductive Potential: Informing Interventions
Bibliographic record
Abstract
Objective: To explore the effect of community-specific risk and protective factors on risky alcohol consumption and vulnerability to having an alcohol-exposed pregnancy in women within a Southern California American Indian community. Methods: A sample of 343 American Indian women of childbearing age was enrolled in a study of risky drinking. All participants completed a questionnaire including alcohol consumption, other health behaviors, the T-ACE risky alcohol consumption screen and the PHQ-9 to measure depression and functionality. A subset of 80 women additionally answered focus group-derived questions about why they choose or do not choose to drink. Results: Risk and protective factors varied among sample subgroups. Broadly, factors affecting risk and protection included: depression, perception of other women’s drinking, children/family, perception of risk to the unborn child, and feeling pressured to drink. Women’s drinking was highly influenced by female friends and relatives. Women were most likely to drink with a girlfriend. Nearly 40% of all participants asked felt pressured to drink. Depression was associated with riskier alcohol consumption, less effective contraception, and testing positive for risky drinking using the T-ACE screen. Depressed women were more likely to binge drink because of stress, trauma, and “to escape my problems”, and more likely to have been exposed to trauma including sexual assault. Conclusions: Interventions should incorporate community-specific factors. In the present sample, two separate strategies are indicated by the data: an information campaign to increase women’s awareness of true social norms and the risks of prenatal alcohol-exposure; and screening for and treating depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".