Internet non-use among Canadian Indigenous older adults: Aboriginal Peoples Survey (APS)
Bibliographic record
Abstract
BACKGROUND: Older adults benefit considerably from Internet use, as it can improve their overall health and quality of life, for example through accessing healthcare services and reducing social isolation. The aim of this study is to assess the prevalence and characteristics of Indigenous older adults in Canada who do not use the Internet. METHODS: The Aboriginal Peoples Survey (APS) 2017 was used and analysis was restricted to those above 65 years of age. The main outcome variable was non-use of the internet in a typical month. Multivariable logistic regression was conducted to assess the relationship between each of the sociodemographic, socioeconomic, lifestyle and health factors and internet non-use. RESULTS: The prevalence of Indigenous older adults who reported never using the Internet in a typical month was 33.6% with the highest prevalence reported by residents of the Canadian territories while the lowest prevalence was reported in British Columbia. After adjustment, results indicated that older age (OR = 4.02, 95% CI 3.54-4.57 comparing 80+ to 65-69 years of age), being a male (OR = 1.52, 95% CI 1.41-1.63), married (OR = 1.34, 95% CI 1.25-1.44), and living in rural areas (OR = 1.95, 95% CI 1.79-2.13) increased the odds of not using the Internet. First Nation individuals and those who have a strong sense of belonging to the Indigenous identity were more likely to not use the Internet compared to their counterparts. In addition, those who were less educated (OR = 8.74, 95% CI 7.03-1 0.87 comparing less than secondary education to Bachelor's Degree and above), unemployed (OR = 1.41, 95% CI 1.26-1.57), smoked cigarettes, used marijuana and those with lower self-perceived mental health and unmet health needs were at increased odds of Internet non-use compared to their counterparts. CONCLUSIONS: Findings from this study show that a large proportion of the Indigenous older adults in Canada do not use the internet. It is necessary to address Indigenous communities' lack of internet access and to create interventions that are consistent with Indigenous values, traditions, and goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".