Perceptions of mental health nurses about psychosocial management of depression in adolescents, North West province, South Africa
Bibliographic record
Abstract
BACKGROUND: Depression in adolescents is a multifactorial global public health concern, with devastating consequences on the sufferer. The prevalence of depression amongst this age group is on the rise, and thus there is the need for greater attention. AIM: To explore and describe the perceptions of mental health nurses regarding the psychosocial management of depression in adolescents in North West province, South Africa. SETTING: The study was conducted in two mental healthcare institutions and two mental healthcare units within two general hospitals in North West province, South Africa. METHOD: A qualitative, explorative, descriptive and contextual research design was used in conducting this study. Data were collected through focus group discussions from four groups of mental health nurses from each of the mental healthcare institutions and mental healthcare units with 18 mental health nurses. Data were analysed using Tesch's open coding method. RESULTS: Two themes emerged from the study: comprehensive psychosocial management and involvement of different stakeholders. CONCLUSION: The findings revealed clear psychosocial management for depression in adolescents. Adopting the findings of this study could improve depressive symptoms and curtail the prevalence of depression amongst adolescents in the North West province, 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".