No Young Carer Left Behind: A Two-Phased Study to Understand and Address the Needs of Young Carers from Rural and Urban Communities Before and During Covid-19
Bibliographic record
Abstract
Canada has one of the largest cohorts of young carers aged 15 to 24 who provide unpaid care for a family member. Although the body of research on young carers is growing in Canada, knowledge on the experiences and needs of young carers living in remote and rural communities is almost absent. This study aimed to understand and address the needs of young carers in rural/remote communities to support our community partner’s goal of expanding their resources and support of this underserved population. The study was conducted in two phases with the first phase being a needs assessment and the second phase addressed those needs. In Phase 1 (conducted pre-COVID-19), three focus groups were conducted with young carers from rural and urban communities with 20 young carers participating in total. Six themes were identified: Internet Usage in Daily Life; Finding and Filtering Information; Concerns Related to Internet Use; Social and Mental Support; What Makes Caregiving More Challenging; and Designing Something to Make Caring Easier. During Phase 2 (conducted mid-COVID-19), 2 focus groups were held via Zoom for Healthcare with a mix of rural and urban young carers in each group. One of the focus groups was held with those under 18 years old and the other included those between 18 to 25 years old. Four themes were identified: Responses to Emergencies; Awareness of Emergency Planning; Potential Impact on Planned Behaviour; and Considerations and Suggestions for Improvement.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".