Assessment of variation in the alberta context tool: the contribution of unit level contextual factors and specialty in Canadian pediatric acute care settings
Notice bibliographique
Résumé
BACKGROUND: There are few validated measures of organizational context and none that we located are parsimonious and address modifiable characteristics of context. The Alberta Context Tool (ACT) was developed to meet this need. The instrument assesses 8 dimensions of context, which comprise 10 concepts. The purpose of this paper is to report evidence to further the validity argument for ACT. The specific objectives of this paper are to: (1) examine the extent to which the 10 ACT concepts discriminate between patient care units and (2) identify variables that significantly contribute to between-unit variation for each of the 10 concepts. METHODS: 859 professional nurses (844 valid responses) working in medical, surgical and critical care units of 8 Canadian pediatric hospitals completed the ACT. A random intercept, fixed effects hierarchical linear modeling (HLM) strategy was used to quantify and explain variance in the 10 ACT concepts to establish the ACT's ability to discriminate between units. We ran 40 models (a series of 4 models for each of the 10 concepts) in which we systematically assessed the unique contribution (i.e., error variance reduction) of different variables to between-unit variation. First, we constructed a null model in which we quantified the variance overall, in each of the concepts. Then we controlled for the contribution of individual level variables (Model 1). In Model 2, we assessed the contribution of practice specialty (medical, surgical, critical care) to variation since it was central to construction of the sampling frame for the study. Finally, we assessed the contribution of additional unit level variables (Model 3). RESULTS: The null model (unadjusted baseline HLM model) established that there was significant variation between units in each of the 10 ACT concepts (i.e., discrimination between units). When we controlled for individual characteristics, significant variation in the 10 concepts remained. Assessment of the contribution of specialty to between-unit variation enabled us to explain more variance (1.19% to 16.73%) in 6 of the 10 ACT concepts. Finally, when we assessed the unique contribution of the unit level variables available to us, we were able to explain additional variance (15.91% to 73.25%) in 7 of the 10 ACT concepts. CONCLUSION: The findings reported here represent the third published argument for validity of the ACT and adds to the evidence supporting its use to discriminate patient care units by all 10 contextual factors. We found evidence of relationships between a variety of individual and unit-level variables that explained much of this between-unit variation for each of the 10 ACT concepts. Future research will include examination of the relationships between the ACT's contextual factors and research utilization by nurses and ultimately the relationships between context, research utilization, and outcomes for patients.
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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 | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».