Frequency of Antidepressant Use in Relation to Recent and Past Major Depressive Episodes
Bibliographic record
Abstract
OBJECTIVE: There has been a trend toward increasing antidepressant (AD) use in recent decades. We used data from the National Population Health Survey (NPHS) to determine whether this trend is continuing and to provide updated estimates of the frequency of use. METHODS: The NPHS is a longitudinal general health survey that began collecting data in 1994. The NPHS evaluates past-year major depressive episodes (MDEs) using a brief diagnostic instrument. At each biannual interview (from 1994 to 2006) current medication use is recorded. We estimated the frequency with which ADs were taken by respondents (aged 12 years and older) with and without past-year MDEs. These frequencies were cross-tabulated by sex, year of interview, and the reported duration of symptoms. RESULTS: ADs are taken by about 5.4% of the household population at any point in time. Most respondents taking ADs did not report past-year MDEs but 63.9% of respondents taking ADs in the absence of past-year episodes reported previous episodes or being diagnosed by a health professional with depression. This pattern is consistent with long-term treatment for relapse prevention. The overall frequency of use of ADs is increasing only in respondents without past-year episodes. CONCLUSIONS: AD use among community residents with past-year MDEs is no longer increasing. The continued increase in the overall frequency of use may point toward broadening indications for AD treatment and may indicate that people are taking these medications for longer periods of time.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".