A Comparison of Factors Used by Physicians and Patients in the Selection of Antidepressant Agents
Bibliographic record
Abstract
OBJECTIVES: Involving patients in treatment decisions may reduce premature antidepressant treatment terminations and improve clinical and health economic outcomes. However, a first step is to determine what information to provide to patients to facilitate their decision making. The authors therefore identified, valued, and ranked factors of antidepressant treatment selection that are relevant to patients and compared them with the opinions of general practitioners. METHODS: Matching surveys were developed for patients and physicians with feedback from focus groups. In the patient group, participation was requested from consecutive patients at four family practice sites in Nova Scotia, Canada. Surveys were mailed to 247 randomly selected general practitioners. RESULTS: Surveys were completed by 127 patients and 110 physicians, representing return rates of 70% and 46%, respectively. The most valued of the 12 differentiating factors when selecting an antidepressant, ranked first by both patients and physicians, was common side effects. Also ranked highly by both groups were precautions with antidepressant use, physician antidepressant experience, and discontinuation problems. Groups differed in their ranking of uncommon serious side effects, time since antidepressants were marketed, cost, and dosing schedule. The ranking distributions were significantly different for six of 12 factors between patients and general practitioners (Wilcoxon ranked-sum test). Patient experience with antidepressants did not influence factor value. CONCLUSIONS: The data demonstrate moderate disagreement between patients and general practitioners regarding the relative value of antidepressant selection factors. The effect of this disagreement on treatment adherence and other outcomes requires further investigation, as improving patient-physician concordance regarding antidepressant choices may lead to improved treatment acceptance.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".