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Comparing interprofessional and interorganizational collaboration in healthcare: A systematic review of the qualitative research

2017· review· en· W2767741183 on OpenAlexaff
Marlène Karam, Isabelle Brault, Thérèse Van Durme, Jean Macq

Bibliographic record

VenueInternational Journal of Nursing Studies · 2017
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCINAHLHealth carePsycINFOQualitative researchContext (archaeology)Systematic reviewTeamworkPsychologyConceptual frameworkMEDLINEScopusNursingKnowledge managementSociologyMedicinePolitical sciencePsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Interprofessional and interorganizational collaboration have become important components of a well-functioning healthcare system, all the more so given limited financial resources, aging populations, and comorbid chronic diseases. The nursing role in working alongside other healthcare professionals is critical. By their leadership, nurses can create a culture that encourages values and role models that favour collaborative work within a team context. OBJECTIVES: To clarify the specific features of conceptual frameworks of interprofessional and interorganizational collaboration in the healthcare field. This review, accordingly, offers insights into the key challenges facing policymakers, managers, healthcare professionals, and nurse leaders in planning, implementing, or evaluating interprofessional collaboration. DESIGN: This systematic review of qualitative research is based on the Joanna Briggs Institute's methodology for conducting synthesis. DATA SOURCES: Cochrane, JBI, CINAHL, Embase, Medline, Scopus, Academic Search Premier, Sociological Abstract, PsycInfo, and ProQuest were searched, using terms such as professionals, organizations, collaboration, and frameworks. METHODS: Qualitative studies of all research design types describing a conceptual framework of interprofessional or interorganizational collaboration in the healthcare field were included. They had to be written in French or English and published in the ten years between 2004 and 2014. RESULTS: Sixteen qualitative articles were included in the synthesis. Several concepts were found to be common to interprofessional and interorganizational collaboration, such as communication, trust, respect, mutual acquaintanceship, power, patient-centredness, task characteristics, and environment. Other concepts are of particular importance either to interorganizational collaboration, such as the need for formalization and the need for professional role clarification, or to interprofessional collaboration, such as the role of individuals and team identity. Promoting interorganizational collaboration was found to face greater challenges, such as achieving a sense of belonging among professionals when differences exist between corporate cultures, geographical distance, the multitude of processes, and formal paths of communication. CONCLUSIONS: This review sets a direction to follow for implementing changes that meet the challenge of a changing healthcare system and the transition towards non-institutional care. It also shows that collaboration between nurses and healthcare professionals from different healthcare organizations is still poorly explored. This is a major limitation in the existing scientific literature, especially given the potential role that could be played by nurses in enhancing interorganizational collaboration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.107
metaresearch head score (Gemma)0.193
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.107
Threshold uncertainty score0.567

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1070.193
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0190.021
Science and technology studies0.0030.004
Scholarly communication0.0040.007
Open science0.0030.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.547
GPT teacher head0.738
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations345
Published2017
Admission routes1
Has abstractyes

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