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Record W2797211003 · doi:10.1186/s12913-018-3087-9

Challenges and opportunities of integration of community based Management of Acute Malnutrition into the government health system in Bangladesh: a qualitative study

2018· article· en· W2797211003 on OpenAlexfundno aff
Santhia Ireen, Mohammad Jyoti Raihan, Nuzhat Choudhury, M Munirul Islam, Md Iqbal Hossain, Ziaul Islam, Syed Moshfiqur Rahman, Tahmeed Ahmed

Bibliographic record

VenueBMC Health Services Research · 2018
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersGlobal Affairs CanadaMinistry of Health and Family WelfareDepartment for International DevelopmentInternational Centre for Diarrhoeal Disease Research, BangladeshStyrelsen för Internationellt Utvecklingssamarbete
KeywordsSevere Acute MalnutritionGovernment (linguistics)MedicineHealth administrationCommunity-based managementMalnutritionNursing researchHealth informaticsHealth services researchCommunity healthContext (archaeology)Public healthNursingService delivery frameworkEnvironmental healthBusinessEnvironmental resource managementService (business)Marketing

Abstract

fetched live from OpenAlex

BACKGROUND: Severe acute malnutrition (SAM) in children is the most serious form of malnutrition and is associated with very high rates of morbidity and mortality. For sustainable SAM management, United Nations recommends integration of community based management of acute malnutrition (CMAM) into the health system. The objective of the study was to assess the preparedness of the health system to implement CMAM in Bangladesh. METHODS: The assessment was undertaken during January to May 2014 by conducting document review, key informant interviews, and direct observation. A total of 38 key informant interviews were conducted among government policy makers and program managers (n = 4), nutrition experts (n = 2), health and nutrition implementing partners (n = 2), development partner (n = 1), government health system staff (n = 5), government front line field workers (n = 22), and community members (n = 2). The assessment was based on: workforce, service delivery, financing, governance, information system, medical supplies, and the broad socio-political context. RESULTS: The government of Bangladesh has developed inpatient and outpatient guidelines for the management of SAM. There are cadres of community health workers of government and non-government actors who can be adequately trained to conduct CMAM. Inpatient management of SAM is available in 288 facilities across the country. However, only 2.7% doctors and 3.3% auxiliary staff are trained on facility based management of SAM. In functional facilities, uninterrupted supply of medicines and therapeutic diet are not available. There is resistance and disagreement among nutrition stakeholders regarding import or local production of ready-to-use therapeutic food (RUTF). Nutrition coordination is fragile and there is no functional supra-ministerial coordination platform for multi-sectoral and multi-stakeholder nutrition. CONCLUSION: There is an enabling environment for CMAM intervention in Bangladesh although health system strengthening is needed considering the barriers that have been identified. Training of facility based health staff, government community workers, and ensuring uninterrupted supply of medicines and logistics to the functional facilities should be the immediate priorities. Availability of ready-to-use therapeutic food (RUTF) is a critical component of CMAM and government should promote in-country production of RUTF for effective integration of CMAM into the health system in Bangladesh.

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)0.009
metaresearch head score (Gemma)0.010
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 score0.020
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0100.007
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.247
GPT teacher head0.482
Teacher spread0.235 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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