Experiences of Family Members of Persons With Mental Illness: A Qualitative Inquiry
Bibliographic record
Abstract
The shortage of skilled and experienced health workers, lack of facilities, limited psychiatric care and inaccessible mental health care services in the uMsunduzi Municipality in Northern KwaZulu-Natal are causes of concern. With limited access to health services and resources, family members have no choice, but to take care of mentally ill relatives. These underlying problems have warranted the need to explore the experiences of family members living with mentally ill relatives. A qualitative, exploratory, descriptive design was used to collect data by in-depth one-on-one interviews and findings were analyzed using Tesch’s method of data analysis. This study showed that the uMsunduzi Municipality needed assistance with resources to support family members living with their mentally ill relatives and family members’ lack of knowledge and experience emerged as a major factor that influenced the care, treatment and rehabilitation of their mentally ill relatives. Compounded by inadequate mental health facilities and infrastructure as well as the implications of the non-implementation of the acts, policies, processes and procedures in the uMsunduzi Municipality; this study recommends the need to enhance community education of all health professionals, providing relevant training in mental illness management. A shared decision-making process is vital, so that a collaborative partnership between family members and health professionals across KZN is established. This will in turn enhance the lived experiences of family members and mentally ill patients.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".