Factors Influencing Adherence to Antipsychotic Medications in Women with DelusionalDisorder: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Adherence to medication regimens is of great importance in psychiatry because drugs sometimes need to be taken for long durations in order to maintain health and function. OBJECTIVE: This study aimed to review influences on adherence to antipsychotic medications, the treatment of choice for the delusional disorder (DD), and to focus on adherence in women with DD. METHODS: This is a non-systematic narrative review of papers published since 2000 using PubMed and Google Scholar, focusing on women with DD and medication adherence. RESULTS: Several factors have been identified as exerting influence on adherence in women with persistent delusional symptoms who are treated with antipsychotics. Personality features, intensity of delusion, perception of adverse effects, and cognitive impairment are patient factors. Clinical time spent with the patient, clarity of communication, and regular drug monitoring are responsibilities of the health provider. Factors that neither patient nor clinician can control are the social determinants of health, such as poverty, easy access to healthcare, and cultural variables. CONCLUSION: There has been little investigation of factors that influence adherence in the target population, e.g., women with DD. Preliminary results of this literature search indicate that solutions from outside the field of DD may apply to this population. Overall, a solid therapeutic alliance appears to be the best hedge against nonadherence.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".