Characteristics of online treatment seekers interested in a text messaging intervention for problem drinking: adults 51 and older versus middle-aged and younger adults
Bibliographic record
Abstract
According to the Institute of Medicine, the vast older adult population is estimated to have mental health and substance use disorders at unprecedented rates and will place high demand on an unprepared healthcare system. Online and mobile health interventions, such as text messaging, could provide an alternative form of frontline intervention that could alleviate some of the burden on the healthcare system; however, it remains unknown what are characteristics of adults over 50 who might be interested in a mobile health behavioral intervention and how they may differ from their younger counterparts. To explore the characteristics of those interested in a text messaging intervention by age, we examined screening data for a randomized controlled trial testing a text messaging intervention to reduce drinking among 1,128 hazardous and problem drinkers, aged 21-30, 31-50, and 51 and older. Participants were recruited online through website advertising on alcoholscreening.org and moderationmanagement.org. Results demonstrated that over a quarter of individuals pursuing online and/or text messaging treatment were 51 and older. These participants reported heavy drinking, with significantly greater number of days drinking and binge drinking than the younger groups, but with fewer consequences. Across age groups, a vast majority of participants were female. Findings demonstrate that a group of adult heavy drinkers 51 and older already pursue online treatment and are interested in using a text messaging intervention to help them reduce drinking, suggesting an avenue to engage this population using an alternative frontline treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".