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Record W2973099788 · doi:10.1017/s0714980819000539

Perspectives about Interprofessional Collaboration and Patient-Centred Care

2019· article· en· W2973099788 on OpenAlexaff
Sherry Dahlke, Kathleen F. Hunter, Maya R. Kalogirou, Kelly A. Negrin, Mary Fox, Adrian Wagg

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2019
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsYork UniversityUniversity of Alberta
Fundersnot available
KeywordsNursingTriangulationPerceptionInterprofessional educationMedicinePsychologyQuality (philosophy)Health careMedical education

Abstract

fetched live from OpenAlex

Interprofessional collaboration is understood to improve efficiencies and quality of care but is associated with challenges such as professionals' differing routines, knowledge, and identities, as well as professional hierarchies and time constraints. Given these challenges, there is limited understanding of how professionals collaborate effectively in providing patient-centred care. This study, with a convergence triangulation mixed-methods study design, explored interprofessional staffs' perceptions of interprofessional collaboration and patient-centred care when working with hospitalized older adults. Thirty-six staff responded to a survey which included the Patient-Centred Care measure and the Modified Index of Interdisciplinary Collaboration; we also interviewed 14 nursing staff. Although all scores suggested a high value was placed on interprofessional collaboration, scores were low related to activities that facilitated team processes. We identified three themes from the data: knowing the patient/family, functional needs, and communication processes. Staff identified daily rounds with interprofessional teams as supportive of interprofessional collaboration and patient-centred-care.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.294
Teacher spread0.285 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations78
Published2019
Admission routes1
Has abstractyes

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Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicInterprofessional Education and CollaborationFrench-language works237,207