What Statistics Canada Survey Data Sources are Available to Study Neurodevelopmental Conditions and Disabilities in Children and Youth?
Notice bibliographique
Résumé
Researchers with an interest in examining and better understanding the social context of children suffering from neurodevelopmental disabilities can benefit by using data from a wide variety of Statistics Canada surveys as well as the information contained in administrative health databases. Selective use of a particular survey and database can be informative particularly when demographics, samples, and content align with the goals and outcomes of the researcher’s questions of interest. Disabilities are not merely conditions in isolation. They are a key part of a social context involving impairment, function, and social facilitators or barriers, such as work, school and extracurricular activities. Socioeconomic factors, single parenthood, income, and education also play a role in how families cope with children’s disabilities. Statistics indicate that five per cent of Canadian children aged five to 14 years have a disability, and 74 per cent of these are identified as having a neurodevelopmental condition and disability. A number of factors must be taken into account when choosing a source of survey data, including definitions of neurodevelopmental conditions, the target group covered by the survey, which special populations are included or excluded, along with a comparison group, and the survey’s design. Surveys fall into categories such as general health, disability-specific, and children and youth. They provide an excellent opportunity to look at the socioeconomic factors associated with the health of individuals, as well as how these conditions and disabilities affect families. However rich the information gleaned from survey data, it is not enough, especially given the data gaps that exist around the health and well-being of children and older youths. This is where administrative and other data can be used to complement existing data sources. Administrative data offer specific information about neurological conditions that won’t be collected in general population surveys, given the nature of such surveys. While researchers can glean information from survey data such as functional health and disability, social inclusion or exclusion, and the role of social determinants in the lives of these children and their families, administrative data can identify rare neurodevelopmental conditions and disabilities not captured in general surveys. Analyzing information from all these sources can lead to a more nuanced understanding of the economic and social impacts, and functional limitations in daily living, that patients and their families experience with certain neurodevelopmental conditions and disabilities. Statistics Canada surveys offer a plethora of information for researchers interested in neurodevelopmental disabilities and social determinants of health. As these surveys are national in their scope, they provide a wealth of information for statistical analysis from people across Canada. This information can be used to inform researchers, policy makers, and families of people who live with neurodevelopmental conditions and disabilities. For example, sophisticated microsimulation modelling techniques have been conducted to project the health and economic impacts from such disabilities 20 years into the future. Such projections will be vital for policy-makers tasked with designing services and programs to assist these people. Much work remains to be done, however. Statistics Canada has already begun working on the potential for using administrative data to conceptualize childhood disability, as well as using data that has been anonymized in national administrative databases to study the health of Canada’s children. These are excellent bases from which to build future research.
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 distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».