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Record W2322074410 · doi:10.1097/acm.0000000000000178

Potential Benefits of Collaboration in Short-Term Global Health Learning Experiences

2014· letter· en· W2322074410 on OpenAlexaffabout
Lawrence C. Loh, Henry C. Lin

Bibliographic record

VenueAcademic Medicine · 2014
Typeletter
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsPublic relationsCurriculumHealth careMedical educationGeneral partnershipMedicineStandardizationNursingPolitical sciencePsychologyPedagogy

Abstract

fetched live from OpenAlex

To the Editor: Rassiwala and colleagues1 provide a valuable overview of short-term global health learning experiences abroad, reviewing two differing models. We particularly appreciated the authors’ recognition of the potential harms to host communities related to the care provided, and welcomed their call to standardize global health education curricula. Despite their focus “on strategy for students […] rather than provision of care,” we believe our colleagues will agree that curricula standardization also needs to focus on the quality of care provided, and we draw on our own experience to suggest ways to achieve this. At present, the first short-term rotation model described by the authors has immense benefits for participating learners and sending institutions, while care provided (even with best intentions) has limited benefits and potential harms for the receiving community abroad.2 As these short-term models become popular, our research group believes that standards are needed to shift this “balance of benefits” more towards the intended service recipients (i.e., the community abroad) while retaining benefits derived by learners and sending institutions. Recognizing that isolated short-term global health learning experiences have limited community benefit, we are developing a “crowdsourcing” model that pools multiple visiting teams into a larger, coordinated effort addressing the root causes of ill health. More than 30 teams visit La Romana, Dominican Republic, annually, providing limited primary care in the form of mobile medical clinics over one to two weeks through a partnership with the local Good Samaritan Hospital. In isolation, these groups are hampered by duplication of efforts, mixed messages, and limited outcomes. Our pilot uses a collaborative model that links together visiting team efforts with those of local leadership in deploying longer-term development projects. Our premise is that multiple coordinated short-term teams could result in a more sustained, meaningful impact. For example, instead of having 12 teams going it alone and handing out assorted pills, could they instead provide a 12-week curriculum to host community providers and help develop local capacity—in essence, strengthening the local health care system by targeting upstream goals? A collaborative model is one example of putting the receiving community’s needs at the center of global health curriculum development, in the hopes of matching the intentions of these short-term learners with meaningful outcomes. Participation and interest in such efforts continue to grow among volunteers and institutions, given the learning, outreach, and advocacy benefits they derive. Realizing this, an informed discussion about models for short-term global health learning experiences is vital in ensuring such experiences also provide lasting benefit for the host communities. Lawrence C. Loh, MD, MPH Adjunct lecturer, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada, clinical lecturer, University of British Columbia, Vancouver, British Columbia, Canada, and cofounder, The 53rd Week, Brooklyn, New York; [email protected] Henry C. Lin, MD Attending physician, Department of Gastroenterology, Hepatology, and Nutrition, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, and cofounder, The 53rd Week, Brooklyn, New York.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.077
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0080.009
Open science0.0040.006
Research integrity0.0130.016
Insufficient payload (model declined to judge)0.0100.003

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 head0.029
GPT teacher head0.371
Teacher spread0.343 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreCommentary

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

Citations1
Published2014
Admission routes2
Has abstractyes

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