Caregiving and its impact on families of the terminally ill
Bibliographic record
Abstract
Changes in the health care system have meant that increasing numbers of the terminally ill receive the majority of their care at home. The purpose of this paper was to document patterns of informal and formal care provided to the terminally ill and assess the impact caregiving has on family members. One hundred and fifty-one family caregivers were recruited for interviews from two community-nursing agencies in an urban region of the province of Ontario, Canada. The majority of respondents 119 (79%) were the female spouses of the patient. The numbers of caregivers providing assistance in specific functional activities were: bathing, 133 (88%); mobility, 123 (81%); dressing and undressing, 114 (76%); toileting, 101(67%), and assistance at night 97 (64%). Sixty-two (41%) respondents reported that they had been providing some form of caregiving for over one year. They also reported that physical demands in caregiving increased substantially during the last three months of the care recipient's life. As family caregivers provided more assistance in activities of daily living they were at greater risk of reporting high caregiver burden. The results of this paper identify the types of care provided by family caregivers of the terminally ill and the impact these demands have on the family caregiver.
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 | 0.007 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".