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Record W2337579556

"I really wanted to be able to contribute something": understanding health science student motivations to create meaningful global health experiences.

2012· article· en· W2337579556 on OpenAlexaboutno aff
Erin Hetherington, Jennifer Hatfield

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsHealth scienceGlobal healthMedical educationPsychologyStudy abroadPublic relationsPedagogyMedicinePolitical sciencePublic healthNursing
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Global health is an area of increasing interest among health professionals, students and educators. This study aims to explore students' motivations and experiences with an undergraduate global health research program in low and middle-income countries and to assess student learning and areas for program improvement. METHODS: All students participating in the Global Health Research Program at the University of Calgary in the summer of 2009 were asked to participate in the study (n=11). In-depth interviews were conducted with students prior to departure and upon their return. Discourse analysis was used to identify interpretive repertoires and to determine how the use of repertoires improves our understanding of students' experiences. RESULTS: Prior to departure, students were highly motivated to "give back" to host communities. Upon return, students felt that their experience had been more about "building relationships" with others than individual contributions to hosts. DISCUSSION: Students' altruistic motivations dominated the discourse, and most students incorporated core concepts from a preparation course only after their international experience. Extensive preparation, supervision and follow-up support can mitigate many of the risks of short-term global health experiences while providing a safe opportunity for significant learning.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.111
GPT teacher head0.391
Teacher spread0.281 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2012
Admission routes1
Has abstractyes

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