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Record W4303684403 · doi:10.1016/j.lansea.2022.100087

A critical look at synergies and fragmentations of universal health coverage, global health security, and health promotion in delivery of frontline health care services: A case study of Bangladesh

2022· review· en· W4303684403 on OpenAlexaff
Malabika Sarker, Puspita Hossain, Syeda Tahmina Ahmed, Mrittika Barua, Ipsita Sutradhar, Syed Masud Ahmed

Bibliographic record

VenueThe Lancet Regional Health - Southeast Asia · 2022
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsMcMaster UniversityImpact
FundersEuropean CommissionWellcome Trust
KeywordsEconomic shortageBusinessHealth promotionScope (computer science)PandemicPublic relationsHealth securityHealth careGlobal healthMedicineEconomic growthEnvironmental healthPolitical sciencePublic healthNursingInfectious disease (medical specialty)Government (linguistics)Coronavirus disease 2019 (COVID-19)DiseaseEconomics

Abstract

fetched live from OpenAlex

Universal Health Coverage (UHC) and Global Health Security (GHS) activities encompass mitigation of risks to health and well-being rights posed by infectious disease outbreaks and facilitated by health promotion (HP) activities. This case study investigated Bangladesh's readiness and capacity to 'prevent, detect and respond' to such outbreaks of an epidemic/pandemic nature. A rapid review of relevant documents, key informant interviews with policymakers/practitioners, and a deliberative dialogue with a crisscross of stakeholders were used to identify challenges and opportunities for 'synergy' among these streams of activities. Findings reveal conceptual ambiguity among respondents about the scope of the three `agendas and their inter-linkages. They perceived the synergy between UHC and GHS superfluous and were obsessed with losing their respective constituencies and resources. Poor coordination among the focal agencies in field activities, lack of supporting infrastructure, and shortage of human and financial resources posed additional challenges for better pandemic/epidemic preparation in future. Funding: This study, "Researching the UHC-GHS-HP Triangle in Bangladesh," was funded by the Wellcome Trust, UK.

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.110
GPT teacher head0.380
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2022
Admission routes1
Has abstractyes

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