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Record W2981580035 · doi:10.1186/s12913-019-4625-9

Implementation fidelity of village health and nutrition days in Hardoi District, Uttar Pradesh, India: a cross-sectional survey

2019· article· en· W2981580035 on OpenAlexafffund
Mira Johri, Louis Rodgers, Cybil Abou-Rizk, Eleanor S. Nash, Alok Mathur

Bibliographic record

VenueBMC Health Services Research · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversité de Montréal
FundersNetworks of Centres of Excellence of CanadaUniversité de MontréalRijksuniversiteit GroningenMinistry of Health and Family WelfareBill and Melinda Gates Foundation
KeywordsMedicinePublic healthNursing researchCross-sectional studyEnvironmental healthGovernment (linguistics)Health administrationHealth informaticsHealth careNursingSocioeconomicsFamily medicineEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: Village Health and Nutrition Days (VHNDs) are a cornerstone of the Government of India's strategy to provide first-contact primary health care to rural areas. Recent government programmes such as the Janani Suraksha Yojana (JSY) and Mission Indradhanush (MI) have catalysed important changes impacting VHNDs. To learn how VHNDs are currently being delivered, we assessed the fidelity of services provided as compared to government norms in a priority district of Uttar Pradesh. METHODS: We fielded a cross-sectional study of VHNDs to provide a snapshot of health services functioning. Process evaluation data were collected via administrative sources, non-participant observation using a standardised form, and structured questionnaires. Questionnaires were designed using a framework to assess implementation fidelity. Key respondents were VHND participants, front-line workers involved in VHND delivery, and VHND non-participants (pregnant women due for antenatal care or children due for vaccination as per administrative records). Results were summarised as counts, frequencies, and proportions. RESULTS: In the 30 villages randomly selected for inclusion, 36 VHNDs were scheduled but four (11.1%) were cancelled and one VHND was not surveyed. Vaccination and antenatal care were offered at 96.8% (30/31) and child weighing at 83.9% (26/31) of VHNDs. Other normed services were infrequently provided or completely absent. Health education and promotion were particularly weak; institutional delivery was the only topic discussed in a majority of VHNDs. The true proportion of any serious problem impeding vaccine delivery was 47.2% (17/36), comprising 4 VHND cancellations and 13 VHNDs experiencing vaccine shortages. Of the 13 incidents of vaccine shortage, 11 related to an unexpected global shortage of injectable polio vaccine (IPV). Over the 31 VHNDs, 37.8% (171 of the 452 scheduled beneficiaries) did not participate. Analysis of missed opportunities for vaccination highlighted inaccuracies in beneficiary identification and tracking and demand side-factors. CONCLUSIONS: The transformative potential of VHNDs to improve population health is only partially being met. A core subset of high-priority services for antenatal care, institutional delivery, and vaccination associated with high-priority government programmes (JSY, MI) is now being provided quite successfully. Other basic health promotion and prevention services are largely not provided, constituting a critical missed opportunity.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.468
Teacher spread0.406 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

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