Identifying Challenges, Enabling Practices, and Reviewing Existing Policies Regarding Digital Equity and Digital Divide Toward Smart and Healthy Cities: Protocol for an Integrative Review
Notice bibliographique
Résumé
BACKGROUND: Digital equity denotes that all individuals and communities have equitable access to the information technology required to participate in digital life and can fully capitalize on this technology for their individual and community gain and benefits. Recent research highlighted that COVID-19 heightened the existing structural inequities and further exacerbated the technology-related social divide, especially for racialized communities, including new immigrants, refugees, and ethnic minorities. The intersection of challenges associated with racial identity (eg, racial discrimination and cultural differences), socioeconomic marginalization, and age- and gender-related barriers affects their access to health and social services, education, economic activity, and social life owing to digital inequity. OBJECTIVE: Our aim is to understand the current state of knowledge on digital equity and the digital divide (which is often considered a complex social-political challenge) among racialized communities in urban cities of high-income countries and how they impact the social interactions, economic activities, and mental well-being of racialized city dwellers. METHODS: We will conduct an integrative review adapting the Whittemore and Knafl methodology to summarize past empirical or theoretical literature describing digital equity issues pertaining to urban racialized communities. The context will be limited to studies on multicultural cities in high-income countries (eg, Calgary, Alberta) in the last 10 years. We will use a comprehensive search of 8 major databases across multiple disciplines and gray literature (eg, Google Scholar), using appropriate search terms related to digital "in/equity" and "divide." A 2-stage screening will be conducted, including single citation tracking and a hand search of reference lists. Results will be synthesized using thematic analysis guidelines. RESULTS: As of August 25, 2022, we have completed a systematic search of 8 major academic databases from multiple disciplines, gray literature, and citation or hand searching. After duplicate removal, we identified 8647 articles from all sources. Two independent reviewers are expected to complete the 2-step screening (title, abstract, and full-text screening) using Covidence followed by data extraction and analysis in 4 months (by December 2022). Data will be extracted regarding digital equity-related initiatives, programs, activities, research findings, issues, barriers, policies, recommendations, etc. Thematic analysis will reveal how barriers and facilitators of digital equity affect or benefit racialized population groups and what social, material, and systemic issues need to be addressed to establish digital equity for racialized communities in the context of a multicultural city. CONCLUSIONS: This project will inform public policy about digital inequity alongside conventional systemic inequities (eg, education and income levels); promote digital equity by exploring and examining the pattern, extent, and determinants and barriers of digital inequity across sociodemographic variables and groups; and analyze its interconnectedness with spatial dimensions and variations of the urban sphere (geographic differences). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40068.
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 | 0,101 | 0,143 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,004 |
| Méta-épidémiologie (sens large) | 0,012 | 0,016 |
| Bibliométrie | 0,026 | 0,021 |
| Études des sciences et des technologies | 0,006 | 0,006 |
| Communication savante | 0,009 | 0,012 |
| Science ouverte | 0,006 | 0,011 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,054 | 0,010 |
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 ».