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Record W4313488177 · doi:10.1080/13561820.2022.2147907

Value of pre-licensure interprofessional education on post-licensure interprofessional collaboration: Perceptions and experiences of practicing professionals

2023· article· en· W4313488177 on OpenAlexaff
Isdore Chola Shamputa, Alison Luke, Clara Kelly, Loretta Waycott, Christy Bishop, John Doucet, Meagan Hatfield, Diana Dupont, Betty Lydon, Tammie Fournier, Marc Nicholson, Shelley Doucet

Bibliographic record

VenueJournal of Interprofessional Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsSaint John Regional HospitalNew Brunswick Community CollegeUniversity of New Brunswick
Fundersnot available
KeywordsInterprofessional educationLicensureThematic analysisMedical educationHealth carePerceptionWorkforceNursingQualitative researchPsychologyMedicineSociology

Abstract

fetched live from OpenAlex

Interprofessional education (IPE) allows students in health professional programs to practice providing collaborative patient care before graduating. Understanding the perceptions and experiences of health care professionals' IPE received prior to entering the workforce is key for improving IPE programs. This study investigated participants' post-licensure interprofessional collaboration (IPC) experiences, how IPE helped prepare them for IPC post-licensure, their perceptions of the IPE they received as students, and their suggestions for improving IPE. This qualitative descriptive study included 20 healthcare workers from seven professions who graduated from two of three co-located post-secondary educational institutions. Data were collected using semi-structured interviews, which were audiotaped and transcribed verbatim. Inductive thematic analysis revealed five themes and six sub-themes: (a) Quality of care; (b) Role clarification; (c) Interpersonal skills (sub-themes: communication and self-confidence); (d) Co-location; and (e) Need for IPE improvements (sub-themes: additional IPE exposures, shadowing experiences, mandatory IPE, and informal peer learning). These findings appear to reinforce the perception that pre-licensure IPE may support the development of skills for IPC among practicing health 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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 score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.458
Teacher spread0.440 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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