MétaCan
Menu
Back to cohort

Ethical issues encountered by medical students during international health electives

2011· article· en· W1566610219 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMedical Education · 2011
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversité de MontréalYork UniversityUniversity of WaterlooMcMaster UniversityHamilton Health Sciences
FundersCanadian Institutes of Health Research
KeywordsDebriefingNonprobability samplingContext (archaeology)Medical educationScope (computer science)PsychologyCurriculumMedicinePedagogy

Abstract

fetched live from OpenAlex

CONTEXT: Medical students increasingly wish to participate in international health electives (IHEs). The authors undertook to understand from the students' perspective the ethical challenges encountered on IHEs in low-resource settings and how students respond to these issues. METHODS: Semi-structured interviews were conducted with 12 medical students upon their return from an IHE. A purposive sampling strategy was used. Inductive data analysis using a constant comparative technique generated initial codes which were later organised into higher-order themes. RESULTS: Five themes relating to ethical issues were identified: (i) uncertainty about how best to help; (ii) perceptions of Western medical students as different; (iii) moving beyond one's scope of practice; (iv) navigating different cultures of medicine, and (v) unilateral capacity building. CONCLUSIONS: International health electives are associated with a range of ethical issues for students. Students would benefit from formal pre-departure training, which should include an evaluation of their expectations of and motivations for participating in an IHE, careful selection of the IHE from amongst the opportunities available, learning about the local context of the IHE prior to departure, and the exploration and discussion of ethical and professionalism issues. Other factors that would benefit students include having an invested onsite colleague or supervisor, maintaining an ongoing connection with the home institution, and formal debriefing on conclusion of the IHE.

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.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.426
Teacher spread0.409 · 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