Older adults’ access to information and referral services using technology in British Columbia, Canada: past learnings and learnings since COVID-19
Bibliographic record
Abstract
Introduction Information and referral services are a significant source of support for older adults. Over the years, there have been discussions about older adults’ access to technology for information and referral services, as these have moved online. Online access to these services has become even more crucial in the context of COVID-19, owing to the requirement for social distancing (Sixsmith, 2020). This chapter reports on a study of older adults’ access to information and referral services using technology between April and August 2020. Community older adult services are the main provider of these services. The study reports on interviews with 28 stakeholders in community older adult services, including staff, volunteers and policy developers across the province of British Columbia, Canada. Participant observation was also utilised in various conference, meetings and service delivery sessions during the study period. Three major themes can be found in the study: these are challenges around access to technology, overcoming challenges around access to technology and collaboration between sectors. We have inferred three theoretical perspectives behind the themes, which are human rights, anti-oppression and intersectionality. These themes and concepts will be further explained. Literature review Populations across the world are ageing. For example, ‘The ageing of Canada's population continues and the average age was 41.4 years on July 1, up slightly from the same day a year earlier (41.3 years). This average has risen every year since comparable record-keeping began in 1971. The share of seniors aged 65 years and older continued to grow, reaching 18.0% on July 1’ (Statistics Canada, 2020). The ageing of populations has been happening alongside the digital revolution that has been transforming global societies and economies in the last decades. This has led to increasing discussion of how technology can be used to support the growing numbers of older people. In Canada, AgeTech (Sixsmith, 2021) refers to, for example, technology-based systems, smart devices and digital communications that have become commonplace, as well as the services, knowledge, skills and operations needed to support and use the technologies. How do we respond to ageing in the context of technological change and the opportunities and challenges affording by emerging technologies? AgeTech is an area that attracts discussions in different disciplines, for example, engineering, information and computer science, and occupational therapy. A key theme within the literature is the use of technology to connect older adults with families, friends and services (Koch, 2010).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".