Transforming Access to Asthma Care in Underserved Communities: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Asthma is a complex chronic illness with significant morbidity and costs, but effective management can prevent these outcomes. The COVID-19 pandemic led to a shift from in-person to virtual healthcare, presenting an opportunity to explore telemedicine in asthma management. METHODS: We performed a scoping review guided by the Arksey and O'Malley framework. We used search terms including: asthma, telehealth, telemedicine, and virtual care, searching four databases (OVID Medline, CINAHL, Web of Science, Embase) from May to July 2023 for publications from 2010 onward, managed in Covidence. Inclusion criteria focused on articles addressing telemedicine accessibility for underserved or vulnerable groups. The selected articles were stratified into mutually exclusive categories: rural communities; lower income populations; lower income rural communities; black, indigenous, people of colour (BIPOC); and English as an additional language (EAL). RESULTS: Out of 811 articles identified in our searches, 171 remained following de-duplication and title/abstract screening. After full-text review, 11 articles remained, stratified into main categories: rural communities (n = 3), lower income (n = 1), lower income rural (n = 2), BIPOC (n = 4), and EAL (n = 1). Most articles (n = 9, 69%) reported a positive association with telehealth use, although barriers like the digital divide (n = 3, 21%) were also noted. CONCLUSION: While telemedicine has a positive effect on asthma care, barriers like inaccessibility remain, thereby limiting full benefits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".