Emergency department triage decision‐making by registered nurses: An instrument development study
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».