Medication Use and Nonadherence to Psychoactive Medication for Mental Health Problems by Community-Living Canadian Seniors with Depression
Bibliographic record
Abstract
OBJECTIVES: To determine the relation between level of depression and psychoactive medication use and nonadherence in Canadian seniors, given that late-life depression is a common, serious mental health problem in Canada. METHODS: Canadian Community Health Survey-Mental Health and Well-Being respondents aged 65 years and older (n = 7,736) comprised the study sample. Using the Composite International Diagnostic Interview to assess depressive symptoms, we created 4 depression levels to capture a spectrum of depressive disorders and (or) symptoms: major depression, comorbid major depression, depressive symptoms, and no depressive symptoms. Psychoactive medications assessed included sleep aids, anxiolytics, and mood stabilizers and (or) antidepressants (AD). Nonadherence was defined as either not taking medication as recommended or taking medication at a lower dosage than prescribed. RESULTS: In total, 22.5% of respondents took psychoactive medication for a mental health problem in the previous 12 months. Psychoactive medication use was 46.8% for major depression, 43.1% for comorbid major depression, 34.0% for depressive symptoms, and 17.6% for no depressive symptoms. Rates of psychoactive medication use ranged from 46.5% of those with major depression, to 17.6% of those with no depressive symptoms. Overall, the rate of nonadherence to psychoactive medication was 31%; rates were highest among those with depressive symptoms (37.4%) and lowest among those with no depressive symptoms (27.4%). All 3 depressive categories were associated with greater odds of use and nonadherence. CONCLUSION: All 3 depression categories were associated with increased use of and nonadherence to psychoactive medication; however, rates of AD and (or) mood stabilizer use for clinically significant depression were low.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".