Forgetfulness: A Role in Noncompliance with Antidepressant Treatment
Bibliographic record
Abstract
OBJECTIVES: To determine the degree of noncompliance with antidepressant treatment in the Alberta population and to investigate the reasons for noncompliance. METHOD: We used data from the Alberta Mental Health Survey, a telephone survey conducted in 2003 (n = 5323 adults), to produce population-based estimates of the frequency of noncompliance and the reported reasons for noncompliance. RESULTS: Reported noncompliance was 41.7% (95% confidence interval [CI], 36.9% to 46.6%) for respondents taking 1, 2, or 3 antidepressants. Noncompliance for those taking 1 antidepressant was 42.0% (95%CI, 36.9% to 47.2%), whereas noncompliance for those taking 2 or 3 antidepressants was 39.4% (95%CI, 26.7% to 53.6%). Among respondents currently taking at least one antidepressant, 64.9% (95%CI, 57.4% to 71.7%) reported that forgetfulness was the most common reason for noncompliance. Of respondents taking 1 medication, 64.1% (95%CI, 56.0% to 71.4%) reported forgetfulness as did 71.3% (95%CI, 48.3% to 86.8%) of those taking 2 or 3 medications. Both the frequency of noncompliance and the reported reasons for noncompliance were independent of sex and age. CONCLUSION: Our study replicates prior reports that indicate that noncompliance is common with antidepressant treatment. Forgetting to take medication is the most important reported reason for this noncompliance.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".