Antidepressant use in older people: family physicians' knowledge, attitudes, and practices.
Bibliographic record
Abstract
OBJECTIVE: To explore the knowledge, attitudes, and practices of primary care physicians regarding treatment of depression in older people. DESIGN: Mailed survey. SETTING: Offices of primary care physicians. PARTICIPANTS: Random sample of 11% of the primary care physicians in Ontario. MAIN OUTCOME MEASURES: Most commonly prescribed antidepressant, maximum dose of this antidepressant, antidepressants avoided, and duration of maintenance therapy. RESULTS: Response rate was 67%. Maximum doses of antidepressants physicians were willing to prescribe were below maximum doses recommended in the 2001 Compendium of Pharmaceuticals and Specialties. Many physicians were not willing to consider titrating the dose of their most commonly prescribed antidepressant beyond the lower half of the therapeutic range even when patients were tolerating the medications without side effects but were not responding to treatment. Two thirds (65%) indicated they would attempt to discontinue antidepressants after 9 months of therapy or less; 50% would discontinue therapy after 6 months or less. This is in contrast to published guidelines recommending maintenance periods of 1 to 2 years. Although fluoxetine is generally avoided in geriatric populations because of its markedly prolonged half-life and potential for drug-drug interactions, 6% of respondents reported prescribing it as a first-line antidepressant. CONCLUSION: With the exception of fluoxetine, most Ontario-based primary care physicians choose appropriate first-line antidepressant medications for their older patients. This study demonstrates that primary care physicians are extremely careful, if not overly cautious, in titrating the dose of antidepressants. Many restrict treatment to lower doses and shorter courses of therapy than dosages and durations recommended for full clinical effect and prevention of relapse. This practice could limit the therapeutic efficacy of that first medication trial, exposing patients to unnecessary medication switches or incomplete therapeutic response when an increased dose might have resulted in a complete resolution of depressive symptoms. Suboptimal management might be the result of ineffective dissemination of guidelines that are often published in subspecialty literature not readily available to primary care physicians.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".