The Importance of Place and Time in Translating Knowledge About Canada's Compassionate Care Benefit to Informal Caregivers
Bibliographic record
Abstract
Canada's Compassionate Care Benefit (CCB), an employment insurance program designed to allow Canadian workers time off to care for a dying relative or friend, has had low uptake since its inception. Due to their role in working with family caregivers, social workers are one group of primary health care professionals who have been identified as benefiting from a knowledge translation campaign. Knowledge tools about the CCB have been developed through social worker input in a prior study. This article presents the findings of a qualitative exploratory intervention. Social workers (n = 8) utilized the tools for 6 months and discussed their experiences with them. Data analysis revealed references to time and space constraints in using to the tools, and demonstrated the impact of time geography on knowledge translation about the CCB. The results suggest that knowledge translation about the CCB could be targeted toward caregivers earlier on in the disease progression before the terminal diagnosis, and knowledge tools must be disseminated to more locations. These results may be valuable to policymakers and palliative care providers, as well as theorists interested in ongoing applications of time geography in knowledge translation and the consumption/production of care.
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| 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".