Perceived Positive Consequences Are Associated with Unhealthy Alcohol Use Among Persons Living with HIV
Bibliographic record
Abstract
ABSTRACT Background: Understanding how perceived positive consequences are associated with drinking may help improve effectiveness of alcohol reduction interventions among people living with HIV (PLWH). We aimed to determine whether perceived positive consequence scores varied by sociodemographic, drinking, mental health or substance use variables. Methods: Perceived positive consequences of drinking were assessed using the PROMIS: Positive Consequences—Short Form. Unhealthy alcohol use was measured using a modified AUDIT-C. We used multiple linear regression to identify factors associated with perceived positive consequence. Results: 328 PLWH who consumed at least one alcoholic beverage in the last 6 months participated in the Florida Cohort study (mean age = 46, 69% male, 58% Black). Perceived positive consequence scores ranged from 0 to 28 (mean = 16.1, SD = 6.9). Perceived positive consequence scores increased by 0.8 points for each 1-point increase in AUDIT-C score. Demographics, thoughts on reducing alcohol use, other substance use, depression, and anxiety were not significantly associated with perceived positive consequences. Conclusions: Our findings suggest perceived positive consequences are associated with unhealthy alcohol use. Positive consequences should be considered with negative consequences in a decisional balance when intervening on alcohol use among PLWH. Contexte: Comprendre comment les conséquences positives perçues sont associées à la consommation d’alcool peut aider à améliorer l’efficacité des interventions de réduction de l’alcool chez les personnes vivant avec le VIH (PVVIH). Nous avons cherché à déterminer si les scores des conséquences positives perçues variaient selon les variables sociodémographiques, de consommation d’alcool, de santé mentale ou de consommation de substances. Méthodes: Les conséquences positives perçues de la consommation d’alcool ont été évaluées à l’aide du PROMIS: Positive Consquences - Short Form -conséquences positives—version abrégée. La consommation d’alcool malsaine a été mesurée à l’aide d’un AUDIT-C modifié. Nous avons utilisé une régression linéaire multiple pour identifier les facteurs associés aux conséquences positives perçues. Résultats: 328 PVVIH ayant consommé au moins une boisson alcoolisée au cours des 6 derniers mois ont participé à l’étude de la cohorte de Floride (âge moyen = 46 ans, 69% d’hommes, 58% de noirs). Les scores des conséquences positives perçues allaient de 0 à 28 (moyenne = 16.1, ET = 6.9). Les scores de conséquences positives perçues ont augmenté de 0.8 point pour chaque augmentation de 1 point du score AUDIT-C. Les données démographiques, les réflexions sur la réduction de la consommation d’alcool, la consommation d’autres substances, la dépression et l’anxiété n’étaient pas associées de manière significative aux conséquences positives perçues. Conclusions: Nos résultats suggèrent que les conséquences positives perçues sont associées à une consommation d’alcool malsaine. Les conséquences positives doivent être considérées avec des conséquences négatives dans le processus décisionnel lors de l’intervention sur la consommation d’alcool chez les PVVIH
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".