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Record W2984015578 · doi:10.1145/3359270

"Parar-daktar Understands My Problems Better"

2019· article· en· W2984015578 on OpenAlexafffund
Sharifa Sultana, Syed Ishtiaque Ahmed, Susan R. Fussell

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.

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)0.004
metaresearch head score (Gemma)0.012
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.008
Scholarly communication0.0050.006
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0190.007

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

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
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

Citations25
Published2019
Admission routes2
Has abstractyes

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