Becoming Adherent to Antipsychotics: A Qualitative Study of Treatment-Experienced Schizophrenia Patients
Bibliographic record
Abstract
OBJECTIVE: Discontinuation of antipsychotic medication is a pervasive clinical problem in the treatment of patients suffering from psychosis. The aim of this study was to complement a largely quantitative body of research by focusing on patients' perspectives on the topic. METHODS: In-depth semistructured interviews were conducted with 20 persons who have schizophrenia spectrum disorders. Narratives were elicited on illness and medication use and emphasized key turning points, such as periods of nonadherence and illness relapses. RESULTS: Respondents had extensive experience with antipsychotic treatment (15±12 years of treatment). Nineteen (95%) reported at least one extended period of nonadherence. A complex picture of medication use or refusal emerged from patients' descriptions. An array of external factors influenced initiation of medication and treatment maintenance: pressure from family or clinicians, secondary benefits from initiating and maintaining treatment, and a variety of coercive measures. Moreover, personal factors transcended rational models in deciding whether to take medication; patients' responses stressed the importance of trust, emotional reactions, and subjective experiences with medication and stigma. CONCLUSIONS: These findings call into question the validity of a purely voluntaristic model of the use of antipsychotic medication. Its use was part of a long and painful fight with a debilitating disorder, and off-medication periods were essential parts of a learning process.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".