Barriers to the Use of Institutional Respite for Alzheimer's Caregivers
Bibliographic record
Abstract
As the incidence of elderly-type illnesses such as Alzheimer's Disease continues to increase along with the elderly population in Canada, the particular health concerns and formal service needs of dementia patients and their caregivers are becoming more apparent and important to researchers, policy analysts, and ministry representatives. Institutional respite is one service that has been consistently underutilized by the Alzheimer's population, but little research has been conducted to determine the reasons behind why this is the case. As part of its Alzheimer strategy, the Ontario Government has promised to invest $7 million annually into respite services for caregivers. It is essential that these monies be used as appropriately as possible and in ways that best assist caregivers, and one of the easiest ways to do this is to include caregiver input in the processes of service evaluation, modification, and development. This study focuses on uncovering the issues that contribute to Alzheimer's caregivers underutilization of institutional respite, with the hope that this paper and like papers in the future will contribute to the development of more generous and more appropriate respite services for families caring for persons with Alzheimer's Disease.
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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".