Psychiatric Care Setting from the Perspective of Psychiatric Nursing Managers
Bibliographic record
Abstract
Background Nursing managers are well-positioned to enhance holistic care for patients in psychiatric settings. Managers need to use evidence-based data available to them when making nurse staffing decisions. Patient classification systems can be an excellent source of patients’ priority care needs. Objective To understand the meaning of using patient classification systems as a management tool for psychiatric nursing managers. Methods Qualitative study with a content analysis methodological framework. Ten nursing managers from psychiatric institutions in the state of São Paulo participated. Data were collected between August 2016 and May 2017 using a semi-structured interview with recorded audio. Results The sample consisted of nine women and one man with an average of 14 years’ experience in mental health and seven years of management experience. The psychiatric care setting emerged as a general theme surrounded by four subthemes: current model of decision making, ideal model of decision making, nursing staff dimensioning/staffing, and professional and mental health legislation. Only half of the managers used a patient classification system as a management tool, and there were difficulties associated with their use of the tool. Conclusion A conceptual model was developed based on the themes, subthemes, categories, and sub-categories in this study. The model demonstrates major differences between psychiatric settings with biomedical models versus psychosocial models. Managers with knowledge of PCS data can better advocate for patients’ holistic needs and adequate nursing resource allocation. Managers may lack the knowledge and skills required for model transformation, and continuing management/leadership education is recommended.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".