Development of a web-based survey on the financial risks of unpaid caregiving: approach and lessons learned from a Canadian perspective
Bibliographic record
Abstract
Little is known about the financial risks of unpaid caregiving. This is, in part, due to challenges in identifying people who are caregivers and limitations in capturing all aspects of spending related to caregiving in existing approaches to public data collection. To fill these gaps, we developed a composite survey informed by validated instruments that assesses the types and magnitude of out-of-pocket expenditures caregivers incur in the provision of homebased care for someone living with a long-term health condition, and their impact across various domains of financial risk. This paper discusses the development of this survey currently in circulation in a Canadian province, and reflects on considerations in the engagement of unpaid caregivers in participatory research. Given its replicability and adaptability, this survey may inform future research in other developed or high-income settings and guide policy attention toward understanding how to protect unpaid caregivers from the financial risks of caring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".