Identifying Challenges, Enabling Practices, and Reviewing Existing Policies Regarding Digital Equity and Digital Divide Toward Smart and Healthy Cities: Protocol for an Integrative Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".