MétaCan
Menu
Back to cohort
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 OpenAlexaff
Shaikh Mehdi Hasan, Kyle Borces, Dipika Shankar Bhattacharyya, Shakil Ahmed, Azam Ali, Alayne M. Adams

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.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.194
Threshold uncertainty score0.385

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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

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 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

Citations10
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueHealth Services InsightsSame topicHealthcare Systems and ReformsFrench-language works237,207