Factors Influencing Nurses' Decisions in Using a Mental Health Triage Scale: A Review
Bibliographic record
Abstract
The aim of this systematic review was to synthesise evidence regarding ED nurses’ decision-making when applying a mental health triage scale. The review sought to answer the question: What factors influence ED nurses’ decisions and decision-making process in applying a mental health triage scale? The views, attitudes and experiences of mental health triage nurses performing triages for patients with mental health presentations in emergency department settings were examined in a systematic review of published and peer-reviewed qualitative research articles. CINAHL, PsycINFO, PubMed, and EMBASE were used to find published works from 2013 to 2022. After reading the title and abstract, the whole text of relevant research was obtained. The results of the included papers were analysed using the thematic content and narrative analysis approach, and critical appraisal of the quality of included articles was carried out using CASP. Sub-themes and themes were created by collapsing emerging patterns and codes. A total of eight full-text studies were included in the review. All the eight articles were qualitative studies conducted in six different countries and published in peer-reviewed journals. The total sample in the included articles consisted of 135 emergency department triage nurses with semi-structured and focus groups used in data collection. The methodological quality of the articles varied, with scores ranging from 16 to 18 out of 20. Three main themes emerged from the systematic review. From the ED triage nurses’ points of view, factors affecting triage decision making for patients with mental health presentations were “nurse-related”, “workplace-related”, and “patient-related”. This is the first systematic review summarising the evidence of the factors affecting ED triage nurses’ decision-making involving patients with mental health presentations. The findings suggest that the nurse as an individual (personally and professional), the workplace (social, structural and architectural environment), and the patient as an individual (safety, risk, acuity and behaviour) affect the quality of nursing decision-making in applying mental health triage scales. Ongoing review of the literature in this area is important to provide further evidence to inform nursing policy, practice, education and further research.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".