Targeting the problem of treatment non-adherence among mentally ill patients: The impact of loss, grief and stigma
Bibliographic record
Abstract
The present study examined the factor structure of the Hungarian version of the Medication Adherence Rating Scale (MARS) and analyzed its association with socio-demographics, insight, internalized stigma, and the experience of loss and grief as a result of the mental illness diagnosis, using confirmatory factor analysis (CFA) with a series of one covariates at a time. Mentally ill patients (N=200) completed self-report questionnaires. CFA supported the original three-factor structure although one item was moved from its original factor to another. Lower insight, higher internalized stigma, loss, and grief were significant predictors of lower treatment adherence. Lower adherence was found to be significantly associated with lower quality of life. No difference in adherence was found between different diagnostic groups, which stresses the need to examine non-adherence in the wider spectrum of mental diagnosis. The study also stresses the importance of patients' subjective experience in promoting better adherence, and raises the need to address the experience of stigma but also of less studied experiences, such as patients' feelings of loss and grief. Integrating these experiences in intervention programs might have meaningful implications for the improvement of treatment adherence and patients' quality of life.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".