Examining access to and use of internet and risk of social isolation during the COVID-19 pandemic among both younger and older adults with intellectual and developmental disabilities in Manitoba
Bibliographic record
Abstract
Persons with intellectual and developmental disabilities (IDD) are found to be at a greater risk for social isolation during COVID-19 pandemic. While existing literature has examined the challenges faced by persons with IDD, there is a dearth of research that addresses the access and usage of digital technologies by persons with IDD as a solution to stay socially connected during the pandemic. This study therefore investigated the access to and use of digital technologies such as the internet in relation to the risk of social isolation experienced by persons with mild intellectual disabilities ID (with or without developmental disabilities) during the COVID-19 Pandemic in Manitoba. Additionally, barriers to the usage of online technology were explored. Data for a sample of 39 adults were collected using an online survey questionnaire, and phone interviews. The key variables in this study are internet use, access to an electronic device, barriers to the internet usage, loneliness, and social isolation. Loneliness was measured using the Revised UCLA (University of California, Los Angeles) Loneliness Scale. To measure social isolation, the MSNA-ID (Maastricht Social Network Analysis – Intellectual Disabilities) tool was used. The study utilized both descriptive and inferential analyses to address the research objectives. We found that the majority of the study participants (89.5%), had access to the internet and possessed some form of electronic devices for online connection. The most commonly used devices were smartphones, desktop computers, and tablets. The majority of the study participants (67.6%) who had access to the internet and an electronic device used the internet for social connection during the COVID-19 pandemic. The majority of the study participants (53.8%) perceived that their internet usage made them feel less isolated. The use of the internet was associated with a feeling of less isolation. The results further revealed the most prevalent barriers to the usage of the internet as reported by the study participants. The cost of internet service/equipment (20.6%), having access to the internet elsewhere (17.6%), difficulty in using the internet (14.7%), and safety concerns (14.7%) were the most commonly barriers reported by the study participants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".