5 The Relationship between National Paediatric Research Funding and Health Outcomes in Canada
Notice bibliographique
Résumé
Abstract Introduction The Canadian Institutes of Health Research (CIHR) is Canada's federal funding agency for health research. CIHR invests approximately $1 billion each year to support health research. A recent cross-sectional analysis of 14,060 paediatric grants from the National Institutes of Health (NIH) showed that funding for paediatric research was correlated with level of disease burden, although certain conditions were identified as either over- or under-funded. Objectives The objective of this study was to determine the relationship between national paediatric research funding and health outcomes in Canada. Methods Health research grants (project, operating, foundation, team, and catalyst grants) related to paediatrics were identified annually for each of 6 financial years (2015-16 to 2020-21) through systematic keyword searches of CIHR’s research funding database. Two researchers extracted data on the disease or topic being studied, and coded each grant according to the top 50 causes of mortality, top 10 causes of hospitalization, and 8 well-being dimensions (as defined by the Canadian Index of Child and Youth Well-being). Inter-rater reliability was first established and then 20% of grants were double-coded and reviewed for consistency. Data were then used to summarize total annual funding for paediatric research and compared to paediatric health outcomes. Results A total of 1703 grants with total funding in the amount of $702,217,490 related to paediatric research were identified in the period between 2015-16 and 2020-21 ($74,944,445 in 2015-16, $87,447,167 in 2016-17, $103,754,359 in 2017-18, $124,277,052 in 2018-19, $132,734,406 in 2019-20, and $179,060,060 in 2020-21). A total of 248 abstracts (14.6% of all included grants) were identified as focused on one of the top 4 leading causes of paediatric mortality. These included accidents ($3,709,863), intentional self-harm (suicide) ($8,529,544), congenital malformations, deformations, and chromosomal anomalies ($30,230,136), and malignant neoplasms ($61,550,990). A total of 379 grant abstracts (22.3%) were identified as focused on a leading cause of paediatric hospitalization. These included disorders related to short gestation and low birth weight ($33,381,731), mood (affective) disorders ($32,236,327), anxiety disorders ($10,445,260), and other mental health disorders ($56,584,714). The top dimensions of well-being identified as being studied were “Feeling happy and respected” (218 grants, $78,315), “Feeling protected” (89 grants, $34,166,781), and “Feeling secure” (83 grants, $27,158,835). Conclusion This analysis indicates that there is general alignment of health research funding in Canada with paediatric mortality, hospitalization, and well-being indicators. Understanding paediatric research funding patterns can help inform prioritization of specific paediatric areas for future strategic funding.
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 | 0,009 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,019 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
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.
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 ».