Coping mechanisms used by the families of mental health care users in Mahikeng sub-district, North West province
Bibliographic record
Abstract
BACKGROUND: Families of the mental health care users (MHCUs) face different challenges in dealing with, supporting and caring for MHCUs on a daily basis. The divergent coping mechanisms that the family members use aim to lower the negative, psychological and emotional impact of the stress. These include: escape, avoidance and denial. AIM: To explore, describe and contextualise coping mechanisms used by the families of MHCUs and to suggest recommendations for improving their coping mechanisms in Mahikeng sub-district, North West province (NWP), South Africa. SETTING: The study was conducted in three community health centres in Mahikeng sub-district, NWP, South Africa. METHODS: A qualitative-exploratory-descriptive and contextual research design was used. Non-probability convenience and purposive sampling techniques were used to select participants. WhatsApp video calls were used to collect data which were analysed following Creswell's six steps of qualitative data analysis. RESULTS: The study established three themes namely; challenges experienced by the family members, coping mechanism used by the family members, and suggestions for improvement in the coping mechanisms for the family members. CONCLUSION: The findings of this study show that the family members of MHCUs are faced with different challenges. Some of the coping mechanisms used by the family members are insufficient and require improvement to enable them to cope effectively. When the coping mechanisms of the family members of MHCUs are improved, their well-being and that of the MHCUs might improve significantly. CONTRIBUTION: The findings of this study provides information that may be used to improve the coping mechanisms of the families of MHCUs in the NWP, South Africa.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".