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Record W4405464797 · doi:10.2478/ijhp-2024-0011

Interprofessional education’s readiness among Brazilian medical students / Interprofessionelle Bildung: Bereitschaft unter brasilianischen Medizinstudierenden

2024· article· en· W4405464797 on OpenAlexaff
Alexandra Secreti Prevedello, Fernanda dos Santos, Emilene Reisdorfer

Bibliographic record

VenueInternational Journal of Health Professions · 2024
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMacEwan University
Fundersnot available
KeywordsBachelorTeamworkMedical educationCurriculumInterprofessional educationHealth carePsychologyScale (ratio)Identity (music)NursingMedicinePedagogyPolitical science

Abstract

fetched live from OpenAlex

Abstract The study examines the readiness for shared learning based on interprofessional education (IPE) among Brazilian medical students participating in preceptorship programs. A total of 642 students from all six medical courses across a state in Brazil completed the Readiness for Interprofessional Learning Scale (RIPLS) and a sociodemographic questionnaire. The results, analyzed across three RIPLS factors—teamwork and collaboration, professional identity, and patient-centered care—reveal a positive inclination toward collaborative learning, though each factor was influenced by different variables. Teamwork and collaboration (factor 1) were significantly associated with gender, medical program semester, prior teamwork experience, and current clinical practice. Professional identity (factor 2) was shaped by gender, prior bachelor’s degree, type of university (public or private), and medical program semester. Patient-centered care (factor 3) showed significant relationships with gender, prior bachelor’s degree, type of university, medical program semester, and current clinical practice. These findings highlight the importance of acknowledging various demographic and educational variables when assessing student readiness for shared learning. Such insights can help medical programs refine their curricula and develop educational strategies to promote IPE, fostering collaborative healthcare practice in alignment with both national and international guidelines.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0090.001

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 head0.027
GPT teacher head0.512
Teacher spread0.485 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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