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Record W4391216398 · doi:10.1371/journal.pone.0266581

Strengthening health service delivery and governance through institutionalizing ‘Urban Health Atlas’—A geo-referenced Information Communication and Technology tool: Lessons learned from an implementation research in three cities in Bangladesh

2024· article· en· W4391216398 on OpenAlexafffund
Sohana Shafique, Dipika Shankar Bhattacharyya, Md. Tarek Hossain, Shaikh Mehdi Hasan, Shakil Ahmed, Rubana Islam, Alayne M. Adams

Bibliographic record

VenuePLoS ONE · 2024
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsMcGill University
FundersInternational Centre for Diarrhoeal Disease Research, BangladeshInternational Development Research Centre
KeywordsBusinessCorporate governanceStakeholderService delivery frameworkGovernment (linguistics)Public healthHealth carePublic relationsService (business)Knowledge managementMedicineMarketingNursingPolitical scienceEconomic growthEconomicsFinance

Abstract

fetched live from OpenAlex

INTRODUCTION: Urban health governance in Bangladesh is complex as multiple actors are involved and no comprehensive data are currently available on infrastructure, services, or performance either in public and private sectors of the healthcare system. The Urban Health Atlas (UHA)-a novel and interactive geo-referenced, web-based visualization tool was developed in Bangladesh to provide geospatial and service information to decision makers involved in urban health service planning and governance. Our objective was to study the opportunities for institutionalization of the UHA into government health systems responsible for urban healthcare delivery and document the facilitators and barriers to its uptake. METHODS: This implementation research was carried out during 2017-2019 in three cities in Bangladesh: Dhaka, Dinajpur and Jashore. During the intervention period, six hands-on trainings on UHA were provided to 67 urban health managers across three study sites. Thirty in-depth and twelve key informant interviews were conducted to understand user experience and document stakeholder perceptions of institutionalizing UHA. RESULTS: Capacity building on UHA enhanced understanding of health managers around its utility for service delivery planning, decision making and oversight. Findings from the IDIs and KIIs suggest that UHA uptake was challenged by inadequate ICT infrastructure, shortage of human resources and lack of ICT skill among managers. Motivating key decision makers and stakeholders about the potential of UHA and engaging them from its inception helped the institutionalization process. CONCLUSION: While uptake of UHA by government health managers appears possible with dedicated capacity building initiatives, its use and regular update are challenged by multiple factors at the implementation level. A clear understanding of context, actors and system readiness is foundational in determining whether the institutionalization of health ICTs is timely, realistic or relevant.

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)0.022
metaresearch head score (Gemma)0.017
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: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0050.004
Scholarly communication0.0040.004
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.188
GPT teacher head0.399
Teacher spread0.211 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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