Knowledge translation resources to support the use of quality of life assessment tools for the care of older adults living at home and their family caregivers
Bibliographic record
Abstract
PURPOSE: To support the use of quality of life (QOL) assessment tools for older adults, we developed knowledge translation (KT) resources tailored for four audiences: (1) older adults and their family caregivers (micro), (2) healthcare providers (micro), (3) healthcare managers and leaders (meso), and (4) government leaders and decision-makers (macro). Our objectives were to (1) describe knowledge gaps and resources and (2) develop corresponding tailored KT resources to support use of QOL assessment tools by each of the micro-, meso-, and macro-audiences. METHODS: Data were collected in two phases through semi-structured interviews/focus groups with the four audiences in Canada. Data were analyzed using qualitative description analysis. KT resources were iteratively refined through formative evaluation. RESULTS: Older adults and family caregivers (N = 12) wanted basic knowledge about what "QOL assessment" meant and how it could improve their care. Healthcare providers (N = 13) needed practical solutions on how to integrate QOL assessment tools in their practice. Healthcare managers and leaders (N = 14) desired information about using patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) in healthcare programs and quality improvement. Government leaders and decision-makers (N = 11) needed to know how to access, use, and interpret PROM and PREM information for decision-making purposes. Based on these insights and evidence-based sources, we developed KT resources to introduce QOL assessment through 8 infographic brochures, 1 whiteboard animation, 1 live-action video, and a webpage. CONCLUSION: Our study affirms the need to tailor KT resources on QOL assessment for different audiences. Our KT resources are available: www.healthyqol.com/older-adults .
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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 | 0.050 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".