Chronic conditions and caregiving in Canada : social support strategies
Bibliographic record
Abstract
While the number of Canadians suffering from chronic illness continues to increase, recent health care cutbacks are resulting in families taking on more responsibility for the care of these patients. This book shows how various forms of social support can benefit those experiencing chronic illness and those involved in caregiving. The text is based on the results of a nationally funded research programme conducted by teams of multidisciplinary researchers. This collection includes assessment studies and four innovative intervention studies involving patients with diabetes, cystic fibrosis, spina bifida, HIV-AIDS, cardiac conditions, and seniors disabled by stroke. The studies evaluate various forms of social support on the basis of a model that is outlined in the first chapter. Several chapters explore barriers to accessing support and key strategies for seeking support. The final chapters summarize the findings and outline the challenges that still face researchers in the field. Some of the forms of support explored by contributors include dyadic peer support for family caregivers of seniors suffering from stroke and heart failure; telephone support for haemophiliacs with AIDS and their caregivers and for parents of children with chronic conditions; and spousal support for coping with cardiac conditions. This volume is an invaluable resource for students, teachers and practitioners in health-related disciplines
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".