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Record W2344078087 · doi:10.1186/s12992-016-0155-y

Coalicion de Salud Comunitaria (COSACO): using a Healthy Community Partnership framework to integrate short-term global health experiences into broader community development

2016· article· en· W2344078087 on OpenAlexaffabout
Lawrence C. Loh, Olga Valdman, Matthew Dacso

Bibliographic record

VenueGlobalization and Health · 2016
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeneral partnershipPublic relationsCommunity healthWork (physics)Social policyInclusion (mineral)Health careEconomic growthPublic healthCommunity organizationHealth services researchCommunity developmentHealth policyPolitical scienceSociologyMedicineNursingEconomicsSocial scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: There is growing concern that short-term experiences in global health experiences (STEGH), undertaken by healthcare providers, trainees, and volunteers from high income countries in lower and middle income countries, risk harming the community by creating a parallel system of care separate from established community development efforts. At the same time, the inclusion of non-traditional actors in health planning has been the basis of the development of many Healthy Community Partnerships (HCP) being rolled out in Canada and the United States. These partnerships aim to bring all stakeholders with a role to play in health to the table to align efforts, goals and programs towards broad community health goals. RESULTS: This methodology paper reports on the process used in La Romana, Dominican Republic, in applying a modified HCP framework. This project succeeded at bringing visiting STEGH organizations into a coalition with key community partners and supported attempts to embed the work of STEGH within longer-term, established development plans. CONCLUSIONS: In presenting the work and process and lessons learned, the hope is that other communities that encounter significant investment from STEGH groups, and will gain the same benefits that were seen in La Romana with regards to improved information exchange, increased cross-communication between silos, and the integration of STEGH into the work of community partners.

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0120.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.225
GPT teacher head0.521
Teacher spread0.296 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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