From diagnosis to end of life: Evolving training and support needs of caregivers of seniors living with Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background No one is prepared for a diagnosis of Alzheimer’s disease, either the person diagnosed or the caregiver. In order to cope with the disease and support their loved ones, caregivers express numerous needs for training and support. But what are these needs at each stage of the disease? Method As part of an action‐research project, 8 focus groups and 28 individual interviews held on the Zoom platform were used to collect the training and support needs of caregivers of seniors living with Alzheimer’s disease in the province of Quebec, Canada. A content analysis inspired by the approach proposed by Miles and Huberman was conducted. Result A total of 52 caregivers, aged between 33 and 85 years old (mean = 61,88; SD = 12,42), identified training and support needs: a) surrounding the diagnosis; b) during the assistance of the loved one at home, with and without cohabitation; c) during accommodation in a long‐term care facility and; d) following the death of the person being cared for. Conclusion Caregivers have training and support needs that evolve according to the trajectory of their loved one’s Alzheimer’s disease. Interventions tailored and personalized to their pathway are necessary to support them in this long trajectory of caregiving.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".