District nurses’ involvement and attitudes to mental health problems: a three‐area cross‐sectional study
Bibliographic record
Abstract
AIMS AND OBJECTIVES: The main aims of this study were to obtain information on the extent of staff contact and input with mental health problems and to determine their experience, training and attitudes to such problems. BACKGROUND: Historical changes and policy shifts have resulted in primary care providers playing an increasing role in the care of mental health problems. Such problems are common within community settings and a major cause of suffering and disability. District nurses in particular are likely to encounter a high level of psychological co-morbidity in their patients. Information is lacking on their involvement, attitudes and specific training for this area of their work. DESIGN AND METHODS: A cross-sectional study was conducted of the staff of district nursing services in three areas, Jersey (Channel Islands), Lewisham and Hertfordshire, using a postal questionnaire. RESULTS: Questionnaires were sent to 331 staff; 66% responded. Community and district nurses estimated a 16% prevalence of mental health problems among their patients, most commonly dementia, depression and anxiety disorders. Staff noted participation in a wide range of psychological care activities, but identified a lack of training for this aspect of their role (three-quarter of nurses had received no such training during the past five years). They reported a willingness to develop their understanding and skills by means of educational programmes. Attitude measures revealed generally optimistic views concerning depression treatment, a rejection of deterministic attitudes about this condition and confidence in the role of district nursing staff in managing such problems. CONCLUSIONS: The need for primary care mental health training is widely noted and based upon consistent evidence of the limited detection and treatment of these problems. This study has employed quantitative methods to clarify the extent and nature of district nursing staff involvement in this area of practice and indicates that training needs are acknowledged by community nurses from geographically distinct settings. RELEVANCE TO CLINICAL PRACTICE: Staff are interested in developing knowledge and skills pertinent to the psychological problems of their patients and their views reveal a consensus that the most important areas for learning are recognition of mental disorders, anxiety management, crisis intervention and pharmacological treatments for depression.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".