Bibliographic record
Abstract
Background: \nDespite the vision of universal access and uptake of digital information and communication technologies (ICT), inequities remain in Canada particularly in rural and Aboriginal populations. This digital divide directly translates to inequities in access and ability to use health services. With nurses increasingly using ICT - in care planning and patient care, in teaching and education for patients and interdisciplinary teams, and in directing patients to ICT-based resources (e.g. information on health promotion and disease management). As key users of ICT for health, nursing has an important role in mitigating the risk of unknowingly exacerbating health disparities and fractured provision of care that can result from the digital divide. \n \nPurpose of study/inquiry: \nThis study explores the digital divide among rural and Aboriginal communities in BC and asks: 1) What proportion of rural people have access to ICT?; and 2) what is the relationship between Aboriginal and non-Aboriginal rural communities with regard to ICT access? \n \nMethodology and methods: \nData will be drawn from the 2010 Canadian Internet Use Survey (Statistics Canada) and Broadband Canada (Industry Canada). A descriptive correlational design will explore trends and factors related to types and frequency of ICT use. \n \nFindings/implications: \nThese preliminary results will provide an overview of the scope and nature of the digital divide in rural and Aboriginal communities in Canada. Rather than assuming universal access to and capacity to use ICT, study results can inform policies and program planning to ensure appropriate use of ICT in the appropriate settings.
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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".