Alcohol misuse, gender and depressive symptoms in community‐dwelling seniors
Bibliographic record
Abstract
OBJECTIVES: Alcohol misuse in seniors has been studied in clinical samples and in small communities, but relatively few studies are population-based. Objectives are: (1) to describe the characteristics of seniors who score 1 or more on the CAGE (Cut down; Annoyed; Guilty; Eye-opener) questionnaire of alcohol problems; (2) to determine if depressive symptoms are associated with alcohol misuse after accounting for other factors. METHODS: Cross-sectional study of community-dwelling older people (65+ years) sampled from a representative population registry in Manitoba, Canada. Participants were initially interviewed in 1991-1992 and reinterviewed in 1996-1997. Data from Time 2 were used; 1,028 persons were included in the analyses. Sociodemographic characteristics, the CAGE questionnaire, Activities of Daily Living (ADLs) and instrumental ADLs (IADLs), the Center for Epidemiologic Studies-Depression (CES-D) scale and the Mini-Mental State Examination (MMSE) were assessed by trained interviewers. RESULTS: Males were more likely to score positive on the CAGE questionnaire. After adjusting for gender, age, and education, there was a strong association between depressive symptoms and alcohol misuse. Poor self-rated health and impairments in IADLs were also associated with alcohol misuse. CONCLUSIONS: Male gender, depressive symptoms, and poor functional status were associated with alcohol misuse in this population-based study. Attention to depressive symptoms and functional status may be important in the care of seniors with alcohol misuse. Alternatively, physicians should enquire about alcohol use in seniors with functional impairment or depressive symptoms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".