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Record W2755555913 · doi:10.3122/jabfm.2017.05.170086

Family Medicine in Ethiopia: Lessons from a Global Collaboration

2017· article· en· W2755555913 on OpenAlexafffundabout
Ann Evensen, Dawit Wondimagegn, Daniel Zemenfes Ashebir, Katherine Rouleau, Cynthia Haq, Abbas Ghavam-Rassoul, Praseedha Janakiram, Elizabeth Kvach, Heidi Busse, James Conniff, Brian M. Cornelson

Bibliographic record

VenueThe Journal of the American Board of Family Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsCalgary Laboratory ServicesUniversity of TorontoUniversity of CalgarySt. Michael's Hospital
FundersFogarty International CenterAddis Ababa UniversityUniversity of Toronto
KeywordsGeneral partnershipMedicineSpecialtyPrimary careMedical educationQuality (philosophy)Public relationsNursingFamily medicinePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Building the capacity of local health systems to provide high-quality, self-sustaining medical education and health care is the central purpose for many global health partnerships (GHPs). Since 2001, our global partner consortium collaborated to establish Family Medicine in Ethiopia; the first Ethiopian family physicians graduated in February 2016. METHODS: The authors, representing the primary Ethiopian, Canadian, and American partners in the GHP, identified obstacles, accomplishments, opportunities, errors, and observations from the years preceding residency launch and the first 3 years of the residency. RESULTS: Common themes were identified through personal reflection and presented as lessons to guide future GHPs. LESSON 1: Promote Family Medicine as a distinct specialty. LESSON 2: Avoid gaps, conflict, and redundancy in partner priorities and activities. LESSON 3: Building relationships takes time and shared experiences. LESSON 4: Communicate frequently to create opportunities for success. LESSON 5: Engage local leaders to build sustainable, long-lasting programs from the beginning of the partnership. CONCLUSIONS: GHPs can benefit individual participants, their organizations, and their communities served. Engaging with numerous partners may also result in challenges-conflicting expectations, misinterpretations, and duplication or gaps in efforts. The lessons discussed in this article may be used to inform GHP planning and interactions to maximize benefits and minimize mishaps.

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)0.003
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 score0.365
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.400
Teacher spread0.337 · 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

Citations10
Published2017
Admission routes3
Has abstractyes

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