Development of the child community health inclusion index: Impact of contextual factors on community inclusion outcomes of children with disabilities
Notice bibliographique
Résumé
Background: Participation and inclusion of children with disabilities in the community is a fundamental human right affirmed by the United Nations’ Convention on the Rights of Persons with Disabilities. Scientific evidence highlights their importance on development and health of children with disabilities. Despite these benefits, children with disabilities are restricted in comparison to that of typically developing children. These restrictions in participation have been linked to environmental. Measuring aspects of the environment that influence a child’s inclusion is crucial to better understand the impact of contextual factors on a child and to inform the development of context-based interventions like public health strategies and policies that aim to improve community inclusion and participation of children with disabilities. Currently, there are no comprehensive measures of community inclusion for children with disabilities addressing the different environmental aspects. Objectives: To develop a method to measure the effects of context-based interventions that aim to promote the participation and inclusion of children with disabilities in the communities where they live. Specific objectives: (i) to systematically review the evidence on context-based interventions that aim to increase participation of children with disabilities in the community; (ii) to develop the content and test the feasibility of a community health inclusion measurement tool that identifies and assesses the barriers and facilitators in different community environments that affect the participation of children with disabilities living in Canada, named Child Community Health Inclusion Index (CHILD-CHII). Methods: Systematic review was conducted. Knowledge was synthesized using the Family of Participation-related Constructs framework and the Community Wellbeing Framework. The content of the CHILD-CHII was developed through a literature review of existing measures and guidelines related to inclusion and item refinement with expert panel consultation. The content was validated through two rounds of a modified e-Delphi technique. The validated measurement tool was tested for feasibility on community facilities by stakeholder evaluators. Results: 11 articles were included for knowledge synthesis. Four higher-level studies showed that context-based interventions had a positive effect on participation and participation-related outcomes. Outcome measures were heterogeneous and high-level evidence was scarce. 189 items from 12 sources were generated for the content of the CHILD-CHII. 48 participants completed the first round of the Delphi technique and 38 completed the second to validate the items. 106 items were presented to the participants, of which 101 items were rated important with high consensus. 17 items were modified for clarity and presented in the second round. In the second round, all 17 modified items were deemed clearer. The validated content of the CHILD-CHII was tested for feasibility. Of the 12 participants, 92% indicated that the tool was ‘long’ or ‘much too long’; 66% indicated that the tool was clear; 58% indicated that the tool was ‘valuable’ or ‘very valuable’. No consensus on difficulty indicator was reached. Conclusion: The CHILD-CHII is a measurement tool that comprehensively identifies and assesses the barriers and facilitators in community facilities and environments that affect the participation of children with disabilities. The appropriate and effective measurement of community inclusion using the CHILD-CHII can be used to estimate child’s inclusion in the community and its health impacts. It can be used to identify areas that can be targeted for interventions with aims to increase inclusion of children with disabilities. Furthermore, the tool can be used as an outcome measure for future studies to explore context-based, systems-level interventions targeting facilities and communities
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 | 0,016 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,009 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».