The strengths of families in supporting mentally-ill family members
Bibliographic record
Abstract
BACKGROUND: Although families caring for a mentally-ill family member may experience challenges, some of these families may display strengths that help them to overcome difficulties and grow even stronger in caring for their family member. In cases where these families are unable to cope, the mentally-ill family member tends to relapse. This indicated the need to explore the strengths of families that cope with caring for mentally-ill family members. OBJECTIVE: The purpose of this study was to explore and describe the strengths of families in supporting mentally-ill family members in Potchefstroom in the North-West Province. METHOD: A qualitative, explorative, descriptive and contextual design was employed, with purposive sampling and unstructured individual interviews with nine participants. Tesch's eight steps of thematic content analysis were used. RESULTS: Twelve themes emerged from the data. This involved strengths such as obtaining treatment, utilising external resources, faith, social support, supervision, calming techniques, keeping the mentally-ill family member busy, protecting the mentally-ill family member from negative outside influences, creative communication, praise and acceptance. CONCLUSION: Families utilise external strengths as well as internal strengths in supporting their mentally-ill family member. Recommendations for nursing practice, nursing education and for further research could be formulated. Psychiatric nurses should acknowledge families' strengths and, together with families, build on these strengths, as well as empower families further through psycho-education and support.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".