Strategies for relapse prevention among people with schizophrenia in KwaZulu-Natal Province, South Africa: Healthcare providers’ perspectives
Bibliographic record
Abstract
INTRODUCTION: Relapse is a significant challenge among people with schizophrenia and is broadly recognized by the aggravation of positive or negative symptoms, the need for re-hospitalization, more intensive case management, and/or changes in medication. The quality of inpatient care and proper transition to outpatient care are crucial in reducing the risk of relapse. Healthcare providers play vital roles in ensuring the continuity of care after patients are discharged from the hospital. Little is known about the roles of preventing relapse from the perspective of healthcare providers. This study explored the currently existing strategies for preventing relapse from the perspective of healthcare providers. METHODS: We captured the view of healthcare providers providing services to psychiatric patients using a qualitative methodological approach with descriptive phenomenology. We conducted audio-recorded, in-depth interviews with 15 consenting clinical providers from a public psychiatric hospital in Durban, South Africa. To facilitate analysis, we used Dedoose software (SocioCultural Research Consultants, LLC [www.dedoose.com]), and the themes were inducted from the data. RESULTS: Six major themes inducted from the analysis: Preparing patients and caregivers for discharge; Developing consistent and caring therapeutic relationships; Using an active approach to transition; Working with patients and caregivers concurrently; Creating and sustaining interagency connections; and Facilitating alternative forms of treatment. CONCLUSIONS: Discharge planning and preparation are needed to ensure smooth transitions from hospital to outpatient care for relapse prevention. The healthcare system should ensure the availability of human resources for health at all levels of health facilities, and multidisciplinary teamwork will help a successful transition.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".