Nurses’ competence areas in adolescent mental health promotion work in student healthcare
Bibliographic record
Abstract
Adolescents’ mental well-being is affected by a variety of different factors and their mental health has been a subject of great concern worldwide. Research has demonstrated the importance of how we arrange young people’s mental health promotion and preventive actions. The aim of this qualitative study was to examine mental health nurses’ competence areas in adolescent mental health promotion work in student healthcare in one city in southern Finland. The data were collected from semi-structured interviews with six mental health nurses. The data were analyzed using the content analysis method applying deductive and inductive approaches. The results of the study revealed 11 subcategories for the multidisciplinary knowledge area, eight subcategories for the skill-related competence area, four subcategories for the attitudinal competence area, and five subcategories for the personal characteristics area. The analysis demonstrates that nurses need extensive competencies in promoting mental health among adolescents in student healthcare. Especially evidence-based practice, client-centeredness, communication, and the social significance of the work were emphasized in the findings. This information can be utilized in service development and continuing education. Further research is needed on how the work of nurses in student healthcare could be more preventive.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".