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Record W2217038055 · doi:10.3109/13561820.2015.1046159

A comparative study of professional and interprofessional values between health professional associations

2015· review· en· W2217038055 on OpenAlexfundno aff
PaiHsuan Tsou, Julie Shih, Ming‐Jung Ho

Bibliographic record

VenueJournal of Interprofessional Care · 2015
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
FundersCanadian Health Services Research Foundation
KeywordsExcellenceInterprofessional educationHealth professionsAccountabilityEthical codeHealth careHealth professionalsProfessional associationAltruism (biology)Professional conductMedicineMedical educationNursingPsychologyPublic relationsPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

The need for effective interprofessional collaboration to ensure safe patient care is crucial. However, health professions are guided by separate professional codes of conduct. To examine whether professional codes are consistent across professions, this review examines 13 key health professional associations in the United States and compares their values to the guiding principles of interprofessional practice defined by the Interprofessional Professionalism Collaborative (IPC). Findings indicate that all six of the IPC's principles (altruism/caring, excellence, ethics, respect, communication, and accountability) were shared by the majority of professions, with many emphasizing two additional attributes, integrity and justice, suggesting there is room to expand the IPC's core principles. Few associations included interprofessional communication and collaboration in their professional codes. There is potential for associations to promote greater interprofessional collaboration by reshaping their professional frameworks. With many shared values across professions, establishing a common framework of interprofessional professionalism is feasible.

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)0.005
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 categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.347
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.007
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 head0.165
GPT teacher head0.590
Teacher spread0.425 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations21
Published2015
Admission routes1
Has abstractyes

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