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Record W2606114709 · doi:10.1080/13561820.2017.1306497

A case study of healthcare providers’ goals during interprofessional rounds

2017· article· en· W2606114709 on OpenAlexaff
Michael Prystajecky, Tiffany Lee, Sylvia Abonyi, Robert D. Perry, Heather Ward

Bibliographic record

VenueJournal of Interprofessional Care · 2017
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHealth careThematic analysisFocus groupContext (archaeology)NursingAttendanceInterprofessional educationMedicineMedical educationPsychologyQualitative researchBusinessSociologyPolitical science

Abstract

fetched live from OpenAlex

Daily interprofessional rounds enhance collaboration among healthcare providers and improve hospital performance measures. However, it is unclear how healthcare providers' goals influence the processes and outcomes of interprofessional rounds. The purpose of this case study was to explore the goals of healthcare providers attending interprofessional rounds in an internal medicine ward. The second purpose was to explore the challenges encountered by healthcare providers while pursuing these goals. Three focus groups were held with healthcare providers of diverse professional backgrounds. Focus group field notes and transcripts were analysed using thematic analysis. The data indicated that there was no consensus among healthcare providers regarding the goals of interprofessional rounds. Discharge planning and patient care delivery were perceived as competing priorities during rounds, which limited the participation of healthcare providers. Nevertheless, study participants identified goals of rounds that were relevant to most care providers: developing shared perspectives of patients through direct communication, promoting collaborative decision making, coordinating care, and strengthening interprofessional relationships. Challenges in achieving the goals of interprofessional rounds included inconsistent attendance, exchange of irrelevant information, variable participation by healthcare providers, and inconsistent leadership. The findings of this study underscore the importance of shared goals in the context of interprofessional rounding.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.057
GPT teacher head0.489
Teacher spread0.432 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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