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Record W2023959686 · doi:10.1503/cjs.027011

Establishing a surgical partnership between Addis Ababa, Ethiopia, and Toronto, Canada

2013· article· en· W2023959686 on OpenAlexaffvenueabout
David W. Cadotte, Michael Blankstein, Abebe Bekele, Selamu Dessalegn, Clare Pain, Miliard Derbew, Mark Bernstein, Andrew Howard

Bibliographic record

VenueCanadian Journal of Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineGeneral partnershipMedical educationSurgical proceduresNursingUnit (ring theory)Resource (disambiguation)Surgery

Abstract

fetched live from OpenAlex

BACKGROUND: Academic partnerships between high- and low/middle-income countries can improve the quality of surgical education and health care delivery in each setting. We report the perceived needs related to collaborative surgical education in a resource-limited setting. METHODS: We used qualitative methods to elicit the opinions of surgical faculty members and surgical residents and quantitative methods to outline surgical procedure type and volume. RESULTS: Ethiopian faculty members identified the management of trauma and emergency surgical care as a priority. They identified supervision in the operating room (OR), topic-specific lectures and supervising resident assessments in the clinic as appropriate roles for partners. Residents were in agreement with faculty members, highlighting a desire for supervision in the OR and topic-specific lectures. CONCLUSION: We present specific experiences and needs of a surgical teaching unit in a low-income country, paving the way to form a meaningful and responsive relationship between 2 surgical departments in 2 universities.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.265
Teacher spread0.226 · 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 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

Citations30
Published2013
Admission routes3
Has abstractyes

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