Primary Caregivers of People with Severe Mental Illness Experience of Anti-Psychotic Medication: Findings from the Semi-Structured Interviews
Bibliographic record
Abstract
BACKGROUND: Management of schizophrenia is now shifted to the community setting and family caregivers are the primary caregivers. Managing medications is a complex responsibility of family caregivers caring for patients with mental illness. Medication compliance contributes to improve health outcomes and reduced hospitalization for the care service users; however, little is known about attitudes and perception of family caregivers.METHODS: A purposeful sample of 21 family caregivers were included in the study. Semi-structured interview was employed to collect data from the participants between May and October 2015. Thematic analysis approach was used to identify the common pattern in the data.RESULTS: Four main themes emerged from the study: insight into illness (poor understanding of illness), treatment factor (thinking about medication, poor guidance for medication compliance), resources and support (availability of medication and cost of medication), health care provider factors (communication gap and poor assessment with follow-up, social dysfunction (social isolation, disruption in life routine).CONCLUSIONS: Responsibility for providing care for patients with mental illness are taken place in the community setting and cared by family caregivers. More information resources are required for this role, which requires specific medication management skills and knowledge.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".