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Record W3012804198 · doi:10.28984/drhj.v3i0.297

Interprofessional Education in Four Canadian Undergraduate Nursing Programs: An Examination of the Supporting Data

2020· article· en· W3012804198 on OpenAlexaffvenueabout
Emily Donato, Nancy Lightfoot, Leigh MacEwan, Lorraine Carter

Bibliographic record

VenueDiversity of Research in Health Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcMaster UniversityLaurentian University
Fundersnot available
KeywordsNursingMedical educationPsychologyMedicine

Abstract

fetched live from OpenAlex

Canadian nursing programs are required to provide Interprofessional Education (IPE) since formal inclusion in the undergraduate curricula in 2012. This multiple case study explored how four undergraduate university nursing programs in Northern Ontario integrated IPE into their curricula, including opportunities and challenges of meeting the new IPE requirements. Data collected and analyzed in the study were: interviews with program directors, focus groups and interviews with faculty members, program documentation and information on websites, and on-site program observations. This paper extends the findings of this study and the themes identified in it. These themes were as follows: 1) varied understandings of IPE, 2) diverse IPE learning activities within curricula, 3) the requirement for support and resources for IPE and research, 4) student participation and leadership in IPE, and 5) limited IPE evaluation (Author names removed for integrity of review process, 2019). In this paper, the themes are explored in further depth through extensive consideration of documentation provided by the involved universities. These resources complement the data derived through interviews and focus groups with faculty and directors. Exploration of these data is a valuable means of illuminating any congruencies and dissonances found in the director and faculty data.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.339
GPT teacher head0.576
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2020
Admission routes3
Has abstractyes

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