Intentional Partnering: How nurse and physician managers in formalized dyads work together to address clinical management issues in a hospital setting
Notice bibliographique
Résumé
Background: In today's healthcare organizations, the pace of technological change, increasing complexity, competitive demands and risks involved in decision making have made it difficult for one individual to lead alone.Collaborative management structures are critical to transforming healthcare delivery and a co-leadership model offers one such approach.Nurses and physicians are uniquely positioned to share the executive roles of co-leadership; however, little is known about how this management dyad operates in the healthcare setting.Most of what is known about the nurse-physician relationship has been based on research at the clinical unit level from the nurses' perspective.Objective: This grounded theory study seeks to explain how nurse and physician managers in formalized "partnerships" work together to address clinical management issues. Methods/Procedures:A nurse-physician management structure (Partnered Management Model) was adopted throughout an urban Canadian university affiliated teaching hospital in 2008 where nurse and physician managers in each division or program were expected to formally "partner" with each other to address clinical management issues.Dyads were purposefully sampled in the Department of Surgery in 2013 on the recommendation of key stakeholders who believed the department effectively illustrated nurse-physician "partnerships".This was followed by theoretical sampling to elaborate on properties of emerging concepts and categories.A total of 36 interviews with 21 participants (12 nurses, 9 physicians) were audio-recorded and transcribed verbatim.The total time spent in observation was 142 hours (110 hours at senior management level and 32 hours at clinical management level) with field notes recorded for 90 observed events.Peer debriefing, informant/participant feedback and an audit trail of all methodological vi decisions ensured the trustworthiness of the data.Constant comparison, open and focused coding, theoretical sensitivity and memos were used in the data analysis.Findings: A substantive theory on intentional partnering was generated.Nurses' and physicians' professional agendas, which included their interests and purposes for working with each other, served as the starting point of intentional partnering.The theory explains how nurse and physician managers align their professional agendas to reap the benefits of partnering through the processes of accepting mutual necessity, daring to risk together and constructing a shared responsibility.Some partners may take the lead or contribute differently in each of the processes.Essential conditions such as being credible, earning trust and safeguarding respect built a foundation for partnering and communicating effectively.Deliberate strategies from senior leadership provided momentum in the intentional partnering process. Conclusions:The theory elucidates the strategizing that underlies the processes as well as the characteristics that influence how the nurse/physician management relationship develops and evolves.The findings may inform the process of developing effective partnerships between nurses and physicians as they take on co-management responsibilities in an evolving healthcare system.The findings may also be applied to health professional management education.
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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 | 0,009 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,006 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».