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Enregistrement W4368348200 · doi:10.1016/j.hlpt.2023.100756

Innovative dashboard for optimising emergency obstetric care geographical accessibility in Nigeria: Qualitative study with technocrats

2023· article· en· W4368348200 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

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Notice bibliographique

RevueHealth Policy and Technology · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGlobal Health Workforce Issues
Établissements canadiensPublic Health OntarioWomen's College HospitalUniversity of Toronto
Organismes subventionnairesGoogle
Mots-clésDashboardTechnocracyBusinessQualitative researchMedicineMedical emergencyComputer scienceData sciencePolitical scienceSociology

Résumé

récupéré en direct d'OpenAlex

• We explored digital dashboards’ potential for planning EmOC geo-access optimisation. • Stakeholders recognise that service planning should be informed by evidence on need. • Politics, pressured community advocacy, and donor funding actually drive planning. • There is a strong appetite for using digital technology to inform service planning. • Stakeholders have concerns about accuracy of data that will inform the digital tool. To explore perspectives of public sector technocrats on the role of and considerations needed for implementing an innovative dashboard that leverages geographic information systems (GIS) in supporting optimisation of emergency obstetric care (EmOC) geographical accessibility in Nigeria. Twenty-three semi-structured interviews were conducted in person or virtually with six policymakers and 17 senior civil servants in Nigeria. Braun and Clarke's six-step approach to thematic analysis, which involved data familiarisation, initial code generation, searching for themes, reviewing themes, defining themes, and producing the report, was applied. Despite recognising the ideal of data-driven needs assessment, in reality, factors such as political pressure, persistent community advocacy, and donor funding drive decisions on siting EmOC facilities. Irregular short-term political cycles and exigencies in health systems prevent new facilities from being established or motivate a focus on facility quality over quantity. There was a strong appetite for using GIS-enabled dashboards to support planning, with enthusiasm for such technology more apparent where innovation was already part of government's philosophy. A digital dashboard that is dynamic, reflective of reality, inclusive of public and private providers, incorporates facility characteristics, and can test accessibility scenarios, was deemed particularly valuable. Its value proposition extended beyond EmOC and provider type. However, its success as a policy tool will depend on the veracity and currency of the data informing it. Technocrats welcome dynamic GIS-enabled dashboards as it offers a significant step-change compared to the current practice for EmOC service planning. Value-for-money of such innovations must be considered if implemented. Planning and siting of emergency services used by pregnant women (EmOC) in many low-resource countries are mostly haphazard. However, there is increasing recognition that technology can refine this process. In this study, we explored perspectives of public sector technocrats in Nigeria on the role of and considerations needed for implementing an innovative digital dashboard that leverages geographic information systems in optimising EmOC geographical accessibility. We found that current planning is mainly driven by political pressure, community advocacy, and donor funding. However, there is a strong appetite in government for using GIS-enabled dashboards to inform service planning, with enthusiasm for such technology appearing to be more grounded in states where innovation was already part of the government's philosophy. Yet, concerns about data accuracy were expressed. Broadly, dashboards that are dynamic, reflective of reality, inclusive of public and private providers, incorporate facility characteristics, and can test access scenarios, were deemed particularly valuable.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,641
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0030,015
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,079
Tête enseignante GPT0,537
Écart entre enseignants0,458 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle