Providing community services for persons with disabilities during the COVID‐19 pandemic: A scoping review
Bibliographic record
Abstract
Community organisations and municipalities support people with disabilities by providing resources and services that are essential for their engagement in the community. Their services were particularly impacted by restrictions related to the COVID-19 pandemic. The aim of the study is to identify scientific literature that examines how community organisations and municipalities adapted services and resources provided to people with disabilities as a result of the COVID-19 pandemic. A scoping review was conducted by searching the databases Medline, Embase, CINAHL, PsycINFO and Web of Science Core Collection in January 2021. Fifteen studies were included from the initial search strategy of 7651 individual studies. Most of the studies were quantitative studies (73.3%; n = 11) and aimed at describing the adaptations put in place during the COVID-19 pandemic (66.7%; n = 10). Most services and resources involved some form of preventive healthcare (66.7%; n = 10). The adaptation of modalities for delivering resources and services varied widely across organisations (e.g. online or a combination of online and in-person) but mostly led to an improvement of the studied outcome (e.g. social skills, quality of life). Barriers (e.g. need for a reliable internet connection, lack of technology literacy from the member) and facilitators (e.g. flexibility and planning from the organisations) for these adaptations have been identified, but there is little information surrounding their cost. The results highlight that the delivery of online services has increased since the inception of the COVID-19 pandemic with valuable outcomes. However, further research is needed to better identify the barriers, facilitators and outcomes of remote services to better face future large-scale disasters like the COVID-19 pandemic and to better support individuals who cannot reach in-person services.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".