INTEREST GROUP SESSION - AGING, ALCOHOL, AND ADDICTIONS: ENGAGING OLDER ADULTS IN CONVERSATIONS ABOUT THEIR OWN ALCOHOL MISUSE
Bibliographic record
Abstract
As increasing numbers of baby boomers reach age 65, they are bringing their drinking and drug use habits with them into late life. Thus, older adults must be included in current efforts to address substance misuse. Healthcare practitioners are being encouraged to incorporate evidence-based approaches, such as Screening, Brief Intervention, Referral to Treatment (SBIRT) and the FRAMES model, into conversations about substance misuse with patients who are at-risk for misusing alcohol. This symposium will include presentations from a multidisciplinary group of researchers involved with training and evaluating approaches used to address alcohol misuse among older adults. Following a brief overview of the SBIRT and FRAMES model, the first presentation will include an examination of findings from a randomized and controlled SBIRT trial in mental health settings, including the effect of age on the efficacy of SBIRT in reducing alcohol use. Next, SBIRT training outcomes with community-based physicians and healthcare providers will be presented with their perspectives on how they might use SBIRT with their older patients. The third presentation will report outcomes from student nurse clinical SBIRT simulation trainings and discuss how student biases and feelings towards substance misuse can influence trainings. Lastly, evaluation outcomes from the UK’s public Drink Wise, Age Well prevention-to-treatment program utilizing the FRAMES approach will be presented with an emphasis on the efficacy of providing brief interventions in public spaces. A discussant will reflect on the importance of engaging older adults in conversations about their own alcohol misuse and implications for clinical and educational applications.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.113 | 0.038 |
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".