Nurses' perceptions of leadership, teamwork, and safety climate in a community hospital in western Canada: A cross-sectional survey design
Notice bibliographique
Résumé
ABSTRACT Patient safety and safety outcomes in hospitals are a major concern. A hospital’s safety climate indicates the degree to which the organization prioritizes patient safety and achieves intended care outcomes. Relationships between nurse managers and frontline nurses and relationships between health care team members are pivotal in promoting a positive safety climate which in turn reduces adverse patient outcomes. Therefore, the purpose of this study was to examine frontline nurses’ perceived relationships with nurse managers and health team members to identify factors associated with safety climate (SC) in a community hospital located in a western Canadian city. The study was guided by Leader-Member Exchange (LMX) theory. Leader-Member Exchange theory postulates that dyadic relationships and work roles develop over time through a series of exchanges between nurse managers and frontline nurses. The study further incorporated Team-member exchange (TMX), a theoretical extension of LMX. Team-Member Exchange was used to guide the study of reciprocal exchanges among nurses and other members of the health care team. A non-experimental, cross-sectional survey design was used to explore the relationship between acute care nurses’ perceived LMX, TMX, and SC. A convenience sampling technique was employed. Licensed practical nurses (LPNs) and registered nurses (RNs) were invited to complete a survey package comprised of four scales. A response rate of 31.1% was achieved with N=105. The majority of respondents were female (89.5%), over 45 years of age, and employed part-time. About half of the respondents were diploma-prepared nurses, whereas the other half had a baccalaureate degree in nursing. Based upon data’s non-normal distribution and various levels of variables, Kruskall Wallis H statistics were used to assess and compare groups in terms of the nurses’ education, gender, length of experience in their current position, specialty experience, organization experience, age, and LMX, TMX, and SC scores. Age was the sole demographic factor that had a statistically significant positive association with LMX and SC. This finding supported the notion that mature nurses enhance the SC. The relationship between TMX, LMX, and SC was explored through Spearman’s rho correlation statistics. LMX and TMX were found to have statistically significant relationships with SC. Multivariable regression analysis was used to identify factors with an association with SC. Nurses’ relationships with team members had a slightly stronger association with SC in comparison with LMX. Over 66% of SC variance was accountable by LMX, TMX, and nurses’ age. This study’s results support the nurse manager who partners with nurses to promote team work to deliver safe patient care and accomplish organizational goals. The presence of strong leadership that incorporates LMX and TMX theories into practice with the reliance upon mature nurses may facilitate the attainment of a positive SC and positive patient outcomes. Further longitudinal studies are recommended to add to the knowledge of the relationships between LMX, TMX, SC and patient outcomes.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».