Assessing the need for caregiver support in Saskatchewan: Gathering perspectives and setting priorities
Bibliographic record
Abstract
/ Résumé Background: An ageing population corresponds with a need for informal caregivers. Caregiving burden is the most compelling problem affecting caregivers of older adults. Previous research efforts have explored predictors of caregiving satisfaction and interventions for caregiving support. Our study aimed to set priorities for the future development of interventions for caregivers in Saskatchewan. Objective: Our objective was to engage caregivers in setting priorities for accessible interventions and support. The specific research question we sought to answer was “What do the experiences of caregivers have to offer in setting priorities for caregiver support?" Methods: We conducted an environmental scan of caregiver intervention programming in Canada. We then held two focus groups with caregivers to older adults, defined as 55 years or older for this study. Twenty-three caregivers attended the first focus group, and ten caregivers participated in the second. We used a qualitative descriptive approach and data were analyzed using thematic analysis. Findings: Caregivers of older adults were eager to share barriers and facilitators to their role. Themes derived from data include (1) lack of access; (2) conflict with self and others; (3) the burden of caregiving; and (4) declining health and wellness. Conclusion: Caregivers may struggle to find resources to support them in their caregiving role. Findings from this study indicate that there is a need for more interventions to support caregivers. Furthermore, our data highlights what outcomes caregivers in Saskatchewan want from those interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".