School nurses’ involvement, attitudes and training needs for mental health work: a UK‐wide cross‐sectional study
Bibliographic record
Abstract
AIM: The aim of this study was to identify school nurses' views concerning the mental health aspects of their role, training requirements and attitudes towards depression in young people. BACKGROUND: Mental health problems in children and young people have high prevalence worldwide; in the United Kingdom they affect nearly 12% of secondary school pupils. School nurses have a wide-ranging role, and identifying and managing mental health problems is an important part of their work. METHODS: A cross-sectional study was conducted using a postal questionnaire sent to a random sample of 700 school nurses throughout the United Kingdom in 2008. Questions concerned involvement in mental health work and training needs for this work. Attitudes were measured using the Depression Attitude Questionnaire. RESULTS: Questionnaires were returned by 258 (37%) nurses. Nearly half of respondents (46%) had not received any postregistration training in mental health, yet 93% agreed that this was an integral part of their job. Most (55%) noted that involvement with young people's psychological problems occupied more than a quarter of their work time. Staff attitudes were broadly similar to those of other primary care professionals, and indicated a rejection of stigmatizing views of depression and strong acknowledgement of the role of the school nurse in providing support. CONCLUSION: Working with young people who self-harm, and recognizing and being better equipped to assist in managing depression and anxiety are key topics for staff development programmes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".