[Relationships between insight and medication adherence in subjects with psychosis].
Bibliographic record
Abstract
BACKGROUND: Poor medication adherence in subjects with psychosis has a high prevalence and a negative impact on clinical outcome. Several studies have reported that a poor level of insight was a strong predictor of poor medi-cation adherence. However, few studies have investigated whether insight was associated with medication adherence, independently from other clinical and treatment characteristics. OBJECTIVE: To explore the link between insight and medi-cation adherence in subjects with psychosis, and to assess the impact of potential confounding factors on this association. METHOD: Subjects included in the study were patients aged 60 or less, consecutively admitted in a psychiatric ward, and presenting with at least one psychotic symptom (delusion or hallucination). Medication adherence was assessed using: 1) history of total discontinuation of treatment against medical advice over the 2 weeks before admission; 2) the 7-point rating scale developed by Kemp et al.; 3) the self-report questionnaire Drug Attitude Inventory (DAI). The Scale to assess Unawareness of Mental Disorder (SUMD) was used to measure level of insight. Assessment of symptoms was performed using the Scale for the Assessment of Positive Symptoms (SAPS), the Scale for the Assessment of Negative Symptoms (SANS), and the Calgary Depression Scale (CDS). DSM IV diagnoses were assessed using the Diagnostic Interview for Psychosis (DIP). The associations between level of insight (SUMD scores) and the three measures of medication adherence were explored using the non-parametric Mann-Whitney and Spearman's tests. Logistic regression models giving Odds Ratios (ORs) and 95% confidence intervals (95% CI) were used to examine the impact of potential confounding variables on the associations between level of insight and medication adherence. RESULTS: 42 patients presenting with schizophrenia broadly defined (n=25) or psychotic mood disorder (n=17) were assessed. Significant associations were found between higher SUMD scores (ie poorer insight) and discontinuation of treatment before admission (z=- 2.6, p=0.009), poor medication adherence rated using the Kemp et al.'s scale (r=- 0.64; p=0.0001), and negative perception of treatment assessed using the DAI (r=- 0.405; p=0.009). The Kemp'scale score and the DAI score were categorised into poor vs. good according to the median for logistic regression analyses. Subjects were 1.7 times more likely (OR=1.7, 95% CI 1.1-2.5, p=0.01) to have discontinued their treatment, 1.9 times more likely (OR=1.9, 95% CI 1.3-2.8), p=0.0003) to have poor medication adherence rated with the Kemp's scale, and 1.8 times (OR=1.8, 95% CI 1.2-2.6, p=0.005) more likely to have a negative perception of the treatment for one point increase at the SUMD score (ie lower level of insight). The associations between SUMD score and the three measures of medication adherence were not modified after adjustment for demographic characteristics (age, gender, educational level, occupational status, marital status) and categorical diagnosis (schizophrenia broadly defined vs. psychotic mood disorder), severity of symptoms (SANS, SAPS, CDS scores), characteristics of the psychotropic treatment, diagnosis of substance or alcohol use disorder, age at onset, and number of previous admissions. CONCLUSION: The study demonstrates that medication adherence is associated with the level of insight, independently from other patient's demographic and clinical characteristics. The association between low level of insight and poor medication adherence should be confirmed using prospective studies carried out in ambulatory patients. These findings suggest that psycho-educational programs aimed at improving insight should be developed in order to improve medication adherence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".