Respite for rural and remote caregivers
Bibliographic record
Abstract
Introduction The complexity of the concept ‘rural’ is well recognised, whether the focus is on size, the sociocultural or the sociopolitical. Even size, arguably the easiest descriptor to deal with, eludes consensus; government definitions range from sizes of 300 to 300,000 (Woods, 2005). In addition, the traditional image of rural as a pastoral setting with conservative values, idyllic slower-paced lives, close-knit communities with flourishing family values and connection to land and locality has been replaced with an acknowledged heterogeneity among rural settings (Giarchi, 2006b). Many aspects of rural communities are now contentious. They vary in terms of distance from urban centres where some are so proximate that they are in the urban shadow; social support may be strong or the exodus of young people might leave a dearth of informal assistance; and longstanding land use and values might be stable or in flux, affected by immigration of both urbanites and mobile transients. Rural communities are undergoing fundamental shifts in their very nature (Woods, 2006). A number of features nevertheless characterise this environment: small populations, low density of population, distance from urban centres, monetary and time costs for travel, and a relative lack of formal transportation, healthcare and other services (CIHI, 2006). The role of family supports is less clear because of contrasting images of rural settings as supportive and expressed concerns, especially for an older population, that the exodus of youth leaves behind a serious lack of family support. In this chapter, attention is directed towards an area of intersection of formal and informal care for older adults in rural areas through an examination of respite. While the concept of respite is a professional term (Brody et al, 1989), the need for a break is one of caregivers’ most frequently expressed needs (Stoltz et al, 2004). Most of the research on caregiver respite has been conducted in urban settings although the concept is equally applicable in rural settings. Arguably, it is of greater importance in those rural settings where youth exodus has left behind fewer others to provide care for older adults. Three different rural regions in Canada offer insight into supporting caregivers in their individual and unique needs for a break in ways that caregivers themselves want. These communities offer innovative formal programmes that facilitate a break for caregivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.143 | 0.018 |
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".