The Saskatchewan Caregiver Experience Study: A Provincial Survey to Gather Perspectives and Set Priorities for Caregiver Support
Bibliographic record
Abstract
Introduction: Policies and services for older adults are increasingly focused on living in the community, rather than relying on institutions. In fact, a total of 70-80% of community care for older adults is provided by informal or family caregivers. With Canada’s aging population, the number of caregivers is growing. The purpose of the Saskatchewan Caregiver Experience Study was to map the experiences and perspectives of caregivers in Saskatchewan and to identify their priority support needs. Method: An online qualitative survey was administered via SurveyMonkey and distributed via Facebook and community newsletters. The survey collected demographics and asked three open-ended questions regarding: (1) the challenges that caregivers experience; (2) the positive aspects of caregiving; (3) the support needs and priorities of Saskatchewan caregivers; as well as a fourth question where caregivers could freely express any other experiences or perspectives. Content analysis was the method used for data analysis. Findings: N=355 caregivers responded to the survey. From the content analysis of each individual question, it was determined that the greatest challenges Saskatchewan caregivers experience are exhaustion, self-doubt, navigating complex systems, living their own lives, and caregiving at a distance. The main positive aspects identified by participants were related to the rewards of caregiving, having the ability and opportunity to care, ensuring quality care for the care recipient, and the experience of personal growth through being a caregiver. Lastly, support priorities of Saskatchewan caregivers were found to be help when they need it, an ear to listen and a shoulder to lean on, assistance in optimizing the care recipient’s health, having healthcare professionals that care, and improved policies, legislations, and regulations. Conclusion: This study highlights the caregiver experience in Saskatchewan. Findings from the Saskatchewan Caregiver Experience Study can be used to create and implement support services and adapt policies to improve the experiences of the population of individuals caring for Saskatchewan’s aging population in the community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".