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Record W2888565472 · doi:10.1016/j.pmedr.2018.08.009

Comprehensive evaluation of male health in four communities in rural Honduras

2018· article· en· W2888565472 on OpenAlexaboutno aff
Jason Galo, Michelle Feeney, Kevin Zambrano, Chelsie Galo, Daniel M. Clinchot

Bibliographic record

VenuePreventive Medicine Reports · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachQuarter (Canadian coin)General partnershipHealth careRural areaMedicineWork (physics)Environmental healthHealth literacyFamily medicineEconomic growthSocioeconomicsGeographyPolitical scienceSociology

Abstract

fetched live from OpenAlex

PODEMOS (Partnership for Ongoing Developmental, Educational and Medical Outreach Solutions) has been a long-standing healthcare provider in 4 communities in northern rural Honduras. In this study, we sought to understand and quantify the health challenges faced by men in the rural communities served by PODEMOS in order to improve the way PODEMOS delivers healthcare. Between June and July of 2015, we conducted 104 structured survey interviews with men 18 years and older in rural Honduras. We found that most men face significant economic limitations in their ability to pay for healthcare and health-determining services and due to low formal education levels face health literacy challenges. Furthermore, we found that a quarter are at risk for health problems due to smoking, and the majority are at risk for musculoskeletal problems due to work in strenuous outdoor labor. However, we found that zero respondents drank alcohol heavily, which is defined as more than 14 drinks in one week. Lastly, we found varying opinions on female contraception use. Our findings indicate that medical brigades to the developing world should understand and quantify the relevant health challenges faced by their target populations.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.120
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.118
GPT teacher head0.394
Teacher spread0.276 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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