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Record W2984015578 · doi:10.1145/3359270

"Parar-daktar Understands My Problems Better"

2019· article· en· W2984015578 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2019
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGovernment (linguistics)Information and Communications TechnologyHealth careEthnographyPublic relationsPopulationBusinessEconomic growthRural areaKnowledge managementPolitical scienceSociologyMedicineComputer scienceEconomicsEnvironmental health

Abstract

fetched live from OpenAlex

This paper discusses the issues that arise while designing better wellbeing support for a low-income rural population in Bangladesh. Through a four-month long ethnographic study, we explored how people in 13villages in southwestern Bangladesh accessed healthcare. Over the course of our fieldwork, we asked the participants about the existing healthcare services available to them and how they interacted with different ICT-based wellbeing support systems. Our findings show that insufficient resources, schedules, and the distant location of government-supported healthcare facilities were major challenges for the villagers. We also found that villagers' limited knowledge and mistrust of care-providing infrastructure block them from the benefits of available ICT-based supports and resources. Drawing on our findings from the field, we discuss possible alternative design directions for improving wellbeing support for rural Bangladeshis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0060.003
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 head0.064
GPT teacher head0.295
Teacher spread0.231 · 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