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Record W3083086483 · doi:10.1177/1178632920951586

Healthcare Systems Strengthening in Smaller Cities in Bangladesh: Geospatial Insights From the Municipality of Dinajpur

2020· article· en· W3083086483 on OpenAlex
Shaikh Mehdi Hasan, Kyle Borces, Dipika Shankar Bhattacharyya, Shakil Ahmed, Azam Ali, Alayne M. Adams

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.

Bibliographic record

VenueHealth Services Insights · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsMcGill University
Fundersnot available
KeywordsEquity (law)Geospatial analysisBusinessCensusHealth careEconomic growthPrivate sectorGeographyInequalityHealthcare systemRegional scienceEnvironmental healthPolitical sciencePopulationMedicineCartographyEconomics

Abstract

fetched live from OpenAlex

Throughout South Asia a proliferation of cities and middle-sized towns is occurring. While larger cities tend to receive greater attention in terms national level investments, opportunities for healthy urban development abound in smaller cities, and at a moment where positive trajectories can be established. In Bangladesh, municipalities are growing in size and tripled in number especially district capitals. However, little is known about the configuration of health services to hold these systems accountable to public health goals of equity, quality, and affordability. This descriptive quantitative study uses data from a GIS-based census and survey of health facilities to identify gaps and inequities in services that need to be addressed. Findings reveal a massive private sector and a worrisome lack of primary and some critical care services. The study also reveals the value of engaging municipal-level decision makers in mapping activities and analyses to enable responsive and efficient healthcare planning.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.399
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.055
GPT teacher head0.258
Teacher spread0.204 · 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