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Record W2574750495 · doi:10.3138/ptc.2015-86gh

Pre-Departure Training for Student Global Health Experiences: A Scoping Review

2017· review· en· W2574750495 on OpenAlexaffvenueabout
Jennifer Bessette, Chantal Camden

Bibliographic record

VenuePhysiotherapy Canada · 2017
Typereview
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsCentre Hospitalier Universitaire de Sherbrooke
Fundersnot available
KeywordsThematic analysisExperiential learningMedical educationMedicineCritical thinkingExperiential knowledgePsychologyIntrospectionNursingPedagogyQualitative researchSociology

Abstract

fetched live from OpenAlex

Purpose: The authors identify the recommended pre-departure training (PDT) practices for physiotherapy students participating in a global health experience (GHE): both the content to be covered and the preferred learning methods to be used. They also discuss the implications of these recommendations for the physiotherapy field. Method: A scoping review of scientific and grey literature was performed to identify the recommended PDT practices. A thematic analysis was then performed to identify emerging themes. Results: The recommended PDT content broke down into the following areas: global health knowledge; ethics, introspection, and critical thinking; cultural competency; cross-cultural communication; placement-specific knowledge; and personal health and safety. The recommended learning methods were a combination of didactic, reflective, and experiential components that would enhance knowledge, develop cross-cultural skills, and address attitudinal changes. Conclusion: The growing participation of Canadian physiotherapy students in GHEs requires universities to adequately prepare their students before they leave to mitigate moral hazards. Given that little empirical research has been published on the effectiveness of PDT, the authors encourage collaborative efforts to develop PDT and evaluate its effectiveness for students and its impact on host communities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.211
GPT teacher head0.572
Teacher spread0.360 · 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 designOther design
Domainnot available
GenreReview

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

Citations16
Published2017
Admission routes3
Has abstractyes

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