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Record W2786073554 · doi:10.15353/joci.v13i3.3329

Bringing Community Back to Community Health Worker Studies: Community interactions, data collection, and health information flows

2017· article· en· W2786073554 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Journal of Community Informatics · 2017
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Community health workersWorkflowHealth careData collectionPerspective (graphical)mHealthCommunity healthKnowledge managementResource (disambiguation)BusinessPublic relationsNursingSociologyMedicinePolitical sciencePublic healthComputer scienceEnvironmental healthEngineeringHealth servicesPopulationPsychological intervention

Abstract

fetched live from OpenAlex

Community Health Workers (CHWs) have the potential to be a great resource in the further growth of the fledging healthcare systems that exist in many developing countries. Through their position as community members, CHWs can interact with other individuals in the areas where they live and work and serve as valuable health resources by providing basic health information and referrals up the healthcare chain. However, few studies have examined CHWs from a community-based perspective. This study analyzes the work and relationships of several CHWs working for the Mashavu mHealth venture in Nyeri, Kenya. Through the use of participant observation and interviews, the workflows of these CHWs were investigated with a specific eye towards interactions between CHWs and their communities and how these interactions affect potential health data collection opportunities. This community-based perspective reveals unique insights into the workflows of the CHWs and how technology might be designed to support them.

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.

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)0.048
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0480.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0320.001
Scholarly communication0.0020.012
Open science0.0170.018
Research integrity0.0000.011
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 head0.227
GPT teacher head0.411
Teacher spread0.185 · 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