Evidence map›Paper›PMID 32503479›Full record

ArticleBMC public health2020

Global health systems partnerships: a mixed methods analysis of Mozambique's HPV vaccine delivery network actors.

Caroline Soi, Jessica Shearer, Baltazar Chilundo, Vasco Muchanga, Luisa Matsinhe, Sarah Gimbel, Kenneth Sherr

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.1field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 2 countries.

Caroline SoiDepartment of Global Health, University of Washington, Harris Hydraulics Laboratory, 1510 San Juan Road, Seattle, WA, 98195, USA. soic@uw.edu.ORCID http://orcid.org/0000-0002-7213-5567
Jessica ShearerPATH, 2201 Westlake Ave, Seattle, WA, 98121, USA.
Baltazar ChilundoUniversidade Eduardo Mondlane, Av. Salvador Allende no. 702, Maputo, Mozambique.
Vasco MuchangaUniversidade Eduardo Mondlane, Av. Salvador Allende no. 702, Maputo, Mozambique.
Luisa MatsinheHealth Alliance International, Rua Caetano Viegas no. 67, Maputo, Mozambique.
Sarah GimbelDepartment of Global Health, University of Washington, Harris Hydraulics Laboratory, 1510 San Juan Road, Seattle, WA, 98195, USA.
Kenneth SherrDepartment of Global Health, University of Washington, Harris Hydraulics Laboratory, 1510 San Juan Road, Seattle, WA, 98195, USA.
Eduardo Mondlane University · MZUniversity of Washington · USHealth Alliance International · USProgram for Appropriate Technology in Health · USSeattle Pacific University · US

Funding

GAVI Alliance Not Applicable
6 · The paper itself

Abstract

backgroundGlobal health partnerships have expanded exponentially in the last two decades with Gavi, the Vaccine Alliance considered the model's pioneer and leader because of its vaccination programs' implementation mechanism. Gavi, relies on diverse domestic and international partners to carry out the programs in low- and middle-income countries under a partnership engagement framework (PEF). In this study, we utilized mixed methods to examine Mozambique's Gavi driven partnership network which delivered human papillomavirus (HPV) vaccine during the demonstration phase.

methodsQualitative tools gauged contextual factors, prerequisites, partner performance and practices while a social network analysis (SNA) survey measured the partnership structure and perceived added value in terms of effectiveness, efficiency and country ownership. Forty key informants who were interviewed included frontline Ministry of Health workers, Ministry of Education staff and supporting partner organization members, of whom 34 participated in the social network analysis survey.

resultsPartnership structure SNA connectivity measurement scores of reachability (100%) and average distance (2.5), were high, revealing a network of very well-connected HPV vaccination implementation collaborators. Such high scores reflect a network structure favorable for rapid and widespread diffusion of information, features necessary for engaging and handling multiple implementation scales. High SNA effectiveness and efficiency measures for structural holes (85%) and low redundancy (30%) coupled with high mean perceived effectiveness (97.6%) and efficiency (79.5%) network outcome scores were observed. Additionally, the tie strength average score of 4.1 on a scale of 5 denoted high professional trust. These are all markers of a collaborative partnership environment in which disparate institutions and organizations leveraged each entity's comparative advantage. Lower perceived outcome scores for country ownership (24%) were found, with participants citing the prominent role of several out-of-country partner organizations as a major obstacle.

conclusionsWhile there is room for improvement on the country ownership aspects of the partnership, the expanded, diverse and inclusive collaboration of institutions and organizations that implemented the Mozambique HPV vaccine demonstration project was effective and efficient. We recommend that the country adapt a similar model during national scale up of HPV vaccination.

Indexed as

Public-Private Sector PartnershipsGlobal HealthGovernment ProgramsHealth PersonnelHealth Plan ImplementationHumansImmunization ProgramsIntersectoral CollaborationMozambiquePapillomavirus InfectionsPapillomavirus VaccinesProgram EvaluationQualitative ResearchPapillomavirus VaccinesDemonstration projectGaviGlobal healthHPV vaccineMozambiquePartnershipSNA

Identifiers

PMID32503479
PMCPMC7275554
OpenAlexW3033890290

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.