Additional file 1 of Spatiotemporal analysis of the effect of global development indicators on child mortality
Notice bibliographique
Résumé
Additional file 1: Table S1. Summary information of countries included in this study. Table S2. Proportion of missingness and number of data points (n=3,510). Table S3. Diagnostics information for space-time interaction model selection. Figure S1. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2000 showing countries with significant observed effects (95% Credible Interval). EDU (Secondary education, pupils (% female)); EMP (Employment to population ratio, 15+, female (%) (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE (Gross national expenditure (% of GDP)); OHP (Out-of-pocket expenditure (% of current health expenditure)); HEXP (Current health expenditure (% of GDP)); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM (PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)). Figure S2. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2005 showing countries with significant observed effects (95% Credible Interval). EDU (Secondary education, pupils (% female)); EMP (Employment to population ratio, 15+, female (%) (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE (Gross national expenditure (% of GDP)); OHP (Out-of-pocket expenditure (% of current health expenditure)); HEXP (Current health expenditure (% of GDP)); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM (PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)). Figure S3. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2010 showing countries with significant observed effects (95% Credible Interval). EDU (Secondary education, pupils (% female)); EMP (Employment to population ratio, 15+, female (%) (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE (Gross national expenditure (% of GDP)); OHP (Out-of-pocket expenditure (% of current health expenditure)); HEXP (Current health expenditure (% of GDP)); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM (PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)). Figure S4. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2017 showing countries with significant observed effects (95% Credible Interval). EDU (Secondary education, pupils (% female)); EMP (Employment to population ratio, 15+, female (%) (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE (Gross national expenditure (% of GDP)); OHP (Out-of-pocket expenditure (% of current health expenditure)); HEXP (Current health expenditure (% of GDP)); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM (PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)). Table S4. Number of countries with missing information from 1960 to 2019 by study variables Number of countries with missing data for all available years by the study variables.
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,729 | 0,079 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».