Reducing substance dependence in elderly people: the side effects program.
Bibliographic record
Abstract
OBJECTIVES: To reduce benzodiazepine, narcotic and total prescription medication use in community-dwelling elderly people with suspected substance dependence. METHODS: A community-based substance dependence program for seniors was established, and referrals were accepted from a wide variety of sources (self-referral, families, emergency departments, family physicians, community agencies, etc). The service typically included several home visits by a nurse and/or social worker trained in recognizing and treating substance dependence in the elderly, medical assessment by a geriatrician and generation of a client-directed treatment plan. Treatment plans included individual and family counselling, recommendations regarding medication changes and involvement in a peer support/education group. Medication use, health care utilization, functional status, cognition and depression scales were collected before and six months after intervention. RESULTS: Of the 95 elderly patients with substance dependence that were seen over a period of 12 months, 55 agreed to participate in the program. Substance dependence included alcohol dependence in 23 participants, benzodiazepine dependence in 20 participants, narcotic dependence in six participants and mixed dependence in six participants. Involvement in the program was associated with significant reductions in depression scale scores (P=0.02), number of daily prescription medications (P=0.002), benzodiazepine use (P=0.01), narcotic use (P=0.04) and number of acute hospitalizations (P=0.001). No significant changes were noted in cognition, functional status, total prescription costs, office visits or emergency room visits. CONCLUSIONS: A community-based sub- stance dependence program for elderly people may significantly reduce narcotic, benzodiazepine and prescription medication use in this population. Further studies are needed to determine the cost effectiveness of such programs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".