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Record W2088548337 · doi:10.1080/13561820802490941

Interprofessional education: A nurse practitioner impacts family medicine residents' smoking cessation counselling experiences

2009· article· en· W2088548337 on OpenAlexafffund
Joan Mitchell, Judith Belle Brown, Carrie Smith

Bibliographic record

VenueJournal of Interprofessional Care · 2009
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsWestern University
FundersSchulich School of Medicine and DentistrySchulich School of Medicine and Dentistry, Western UniversityOntario Ministry of Health and Long-Term Care
KeywordsSmoking cessationInterprofessional educationNursingNurse practitionersMedicineFamily medicineMedical educationHealth care

Abstract

fetched live from OpenAlex

This qualitative research paper describes a successful example of interprofessional education with family medicine residents (FMR) by a nurse practitioner (NP) colleague. The educational impact of the NP role in regard to smoking cessation counselling is revealed by the analysis of 16 semi-structured interviews using a phenomenological approach. The key themes depicted the NP as an educator and mentor, encourager and referral resource. Outcomes of improved knowledge, skills, and motivation towards providing smoking cessation counselling are described. This research provides some understanding of how professional students' learning and practice can be affected by a member of another profession through direct and indirect approaches. The experiences identified how interprofessional education and collaborative clinical practice can affect FMRs' attitudes, knowledge and behaviours. This learning can guide us in enhancing the quality of education provided to all health care professionals.

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, 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 score0.388
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
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.002
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 head0.029
GPT teacher head0.465
Teacher spread0.436 · 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

Citations17
Published2009
Admission routes2
Has abstractyes

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