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Record W2261733452 · doi:10.5750/ijpcm.v5i2.528

Interprofessional Care: Patient Experience Stories

2015· article· en· W2261733452 on OpenAlexaffabout
Kateryna Metersky, Jasna Schwind

Bibliographic record

VenueThe International Journal of Person Centered Medicine · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsToronto Metropolitan UniversityWestern University
Fundersnot available
KeywordsNarrativeNarrative inquiryStorytellingPsychologyTemporalityMetaphorHealth careQualitative researchNursingMedical educationMedicineSociologyArt

Abstract

fetched live from OpenAlex

Interprofessional care (IPC) has been discussed in the literature as having the ability to lower health care expenditures, decrease wait times, enhance patient health outcomes and increase healthcare provider (HCP) satisfaction with care-delivery. To date, limited research has been conducted on patients’ experiences of receiving IPC. Using Connelly and Clandinin’s Narrative Inquiry qualitative research approach, three participants were invited to engage in a modified version of Schwind’s Narrative Reflective Process, a creative self-expression tool that utilizes storytelling, metaphor selection, drawing, creative writing and reflective dialogue. Participants shared their stories, and selected and drew metaphors that best represent for them their experiences of receiving IPC. They were also asked whether or not they believe person-centered care was delivered to them. Collected stories were analyzed as per the three common places of Narrative Inquiry: temporality, sociality and place, as well as the three levels of justification: personal, practical and social. Told stories were examined through the theoretical lens of the National Canadian Interprofessional Competency Framework. Three narrative threads emerged within this study: communication, interprofessional team composition, and patient within interprofessional team. The findings appear helpful to inform educators, HCP, policy makers, and researchers, as they strive to enhance person-centered interprofessional care practice. For patients, a clear opportunity for their voices to be heard has been outlined.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.118
GPT teacher head0.469
Teacher spread0.351 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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