Emergency department triage decision‐making by registered nurses: An instrument development study
Bibliographic record
Abstract
Abstract Aim To develop and psychometrically test the triage decision‐making instrument, a tool to measure Emergency Department Registered Nurses decision‐making. Design Five phases: (1) defining the concept, (2) item generation, (3) face validity, (4) content validity and (5) pilot testing. Methods Concept definition informed by a grounded theory study from which four domains emerged. Items relevant to the four domains were generated and revised. Face validity was established using three focus groups. The target population upon which the reliability and validity of the triage decision‐making instrument was explored were triage registered nurses in emergency departments. Three expert judges assessed 89 items for content and domain designation using a 4‐point scale. Psychometric properties were assessed by exploratory factor analysis, following which the names of the four domains were modified. Results The triage decision‐making instrument is a 22‐item tool with four factors: clinical judgement, managing acuity, professional collaboration and creating space. Focus group data indicated support for the domains. Expert review resulted in 46 items with 100% agreement and 13 with 66% agreement. Fifty‐nine items were distributed to a convenience sample of 204 triage nurses from six hospitals in 2019. The Kaiser–Meyer–Olkin measures indicated that the data were sufficient for exploratory factor analysis. Bartlett's test indicated patterned relationships among the items ( X 2 (231) = 1156.69). An eigenvalue of >1.0 was used and four factors explained 48.64% of the variance. All factor loadings were ≥0.40. Internal consistency was demonstrated by Cronbach's alphas of .596 factor 1, .690 factor 2, .749 factor 3 and .822 for factor 4. Conclusion The triage decision‐making instrument meets the criteria for face validity, content validity and internal consistency. It is suitable for further testing and refinement. Impact The instrument is a first step in quantifying triage decision‐making in real‐world clinical environments. The triage decision‐making instrument can be used for targeted triage interventions aimed at improving throughput and staff education. Statistical Support Dr. Tak Fung who is a member of the research team is a statistician. Statistical Methods Development, validation and assessment of instruments/scales. Descriptive statistics. Reporting Method STROBE cross‐sectional checklist. Implications for the Profession and/or Patient Care The TDI makes the complexity of triage decision‐making visible. Identifying the influence of decision‐making factors in addition to acuity that affect triage decisions will enable nurse managers and educators to develop targeted interventions and staff development initiatives. By extension, this will enhance patient care and safety.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".