The lesser of two evils: a qualitative study of quetiapine prescribing by family physicians
Bibliographic record
Abstract
BACKGROUND: Quetiapine is an antipsychotic that is widely prescribed off-label by family physicians despite evidence that safer alternatives exist. The aim of this research was to explore, in-depth, family physicians' reasons for this behaviour. METHODS: We conducted qualitative interviews with 15 urban family physicians in Alberta between October 2015 and April 2016. Participants were purposively selected based on sex, years of experience and practice type. Interviews explored participants' experiences prescribing quetiapine. Interviews were recorded, transcribed verbatim and coded with the use of thematic analysis. RESULTS: A wish to support day-to-day function of patients with complex psychosocial needs without causing benzodiazepine addiction motivated participants to prescribe quetiapine. The indications were varied and included incomplete symptom resolution, unclear or multiple mental health diagnoses, and complicated psychosocial problems. Family physicians benchmarked their prescribing against peers and were reluctant to stop medication started by colleagues. Limited knowledge of quetiapine's adverse effects led prescribers to choose low dosages. INTERPRETATION: Quetiapine helped family physicians treat patients with complex mental health problems without prescribing benzodiazepines, but awareness of quetiapine's adverse effects was poor. Education about quetiapine should combine psychopharmacology with multidisciplinary educational initiatives that focus on symptom resolution, comorbidity and nondrug options to promote more appropriate prescribing.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".