Mapping the Caregiver Experience in a Canadian Province: Research Methodology for the Saskatchewan Caregiver Experience Study
Bibliographic record
Abstract
BACKGROUND: Policies and services for older adults are increasingly focused on living in the community, rather than relying on institutions. A total of 70-80% of community care for older adults is provided by family and friend caregivers. With Canada's aging population, the number of caregivers to older adults is growing. PURPOSE: The purpose of this paper is to describe the research methodology that was employed in the Saskatchewan Caregiver Experience Study. The methodology was used to map the experiences and gather perspectives of caregivers in Saskatchewan and to identify their priority support needs. METHODS: Qualitative description was the approach in this study. An online qualitative survey was administered via SurveyMonkey and distributed via Facebook and community newsletters. The survey collected caregiver demographics and asked three open-ended questions regarding: (1) the challenges that caregivers experience; (2) the positive aspects of caregiving; and (3) the support needs and priorities of Saskatchewan caregivers. A fourth question where caregivers could freely express any other experiences or perspectives was included. Content analysis was the method used for data analysis. RESULTS: 355 individuals met the inclusion criteria for this study. Participants were evenly distributed amongst urban-large, urban-small/medium, and rural settings in Saskatchewan. The average age of caregivers and care recipients were 61 and 83 respectively. CONCLUSION: This study has implications for research, practice, and policy. By gathering the full spectrum of the caregiver experience in Saskatchewan, this study can help to inform how communities, governments, and our healthcare system can best support caregivers in their role.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".