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Record W2107693033 · doi:10.3109/0142159x.2013.765550

Integrating interprofessional education in community-based learning activities: Case study

2013· article· en· W2107693033 on OpenAlexaff
Somaya Hosny, Mohamed H. Kamel, Yasser El‐Wazir, John Gilbert

Bibliographic record

VenueMedical Teacher · 2013
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInterprofessional educationMedical educationHealth professionsWork (physics)Health careSuez canalPsychologyMedicineNursingPolitical scienceBusinessEngineering

Abstract

fetched live from OpenAlex

Faculty of Medicine/Suez Canal University (FOM/SCU) students are exposed to clinical practice in primary care settings within the community, in which they encounter patients and begin to work within interprofessional health teams. However, there is no planned curricular interaction with learners from other professions at the learning sites. As in other schools, FOM/SCU faces major challenges with the coordination of community-based education (CBE) program, which include the complexity of the design required for Interprofessional Education (IPE) as well as the attitudinal barriers between professions. The aim of the present review is to: (i) describe how far CBE activities match the requirements of IPE, (ii) explore opinions of graduates about the effectiveness of IPE activities, and (iii) present recommendations for improvement. Graduates find the overall outcome of their IPE satisfactory and believe that it produces physicians who are familiar with the roles of other professions and can work in synergy for the sake of better patient care. However, either a specific IPE complete module needs to be developed or more IPE specific objectives need to be added to current modules. Moreover, coordination with stakeholders from other health profession education institutes needs to be maximized to achieve more effective IPE.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0220.000

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 head0.040
GPT teacher head0.479
Teacher spread0.438 · 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

Citations55
Published2013
Admission routes1
Has abstractyes

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