Knowledge to Impact: From Conceptualizing to Mobilizing the Saskatchewan Caregiver Experience Study
Bibliographic record
Abstract
OBJECTIVES: Given the rising number of older adults reliant on family and friend caregivers (i.e., those who provide informal care), the Saskatchewan Caregiver Experience Study aimed to examine the experiences of these caregivers in Saskatchewan and identify unmet needs. This paper describes our knowledge translation and mobilization efforts of our study's findings. METHODS: Researchers partnered with the Saskatoon Council on Aging (SCOA) to conduct the study, recruiting 355 family and friend caregivers. We evaluated impacts across conceptualization, data collection and knowledge mobilization using the Knowledge Engagement Impact Assessment Toolkit to assess how effectively our study's design has the potential to impact policy and practice, which involved completing an Assessment Matrix (quantitative assessment) and Assessment Portrait (qualitative assessment). A stakeholder webinar served as the primary knowledge translation event. FINDINGS: An Assessment Matrix revealed moderate impact scores for conceptualization and knowledge mobilization phases. However, the Assessment Portrait reflected collaboration, thorough policy alignment and outreach. Data collection and analysis scored lower. We reflected on this lower score in the Assessment Portrait as being due to fewer avenues for reciprocal engagement and capacity-building during this stage. Policy recommendations, formed in collaboration with SCOA, were presented at the webinar and called for expanded respite care and streamlined system navigation. CONCLUSION: By systematically evaluating research activities, this study highlights the critical role of knowledge translation in shaping caregiver support. Findings reinforce the importance of early and ongoing stakeholder collaboration, user-friendly dissemination methods and targeted policy action. Employing a structured framework for measuring engagement impact can guide targeted interventions, ensuring that caregiver programming, legislative reforms and improved care quality align with evolving population needs and priorities.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".