Caregiver Employment Status and Time to Institutionalization of Persons with Dementia
Bibliographic record
Abstract
Background - This study was undertaken to examine the association between caregiver employment status and the time to institutionalization of persons with dementia. No study has previously examined this association. Methods - The database of the Canadian Study of Health and Aging was used to obtain data on 326 caregiver/care-recipient dyads. Caregivers were primary, informal carers; care-recipients were diagnosed with dementia and living in the community at baseline. Care-recipients were followed from the date of their baseline screening interview until the date of institutionalization, the date of death before institutionalization, or the date of the 5-year follow-up interview. An accelerated failure time model with a Weibull distribution was used to conduct the survival analysis. Results - During the 5-year follow-up period, 139 care-recipients (45%) were institutionalized; the median time to institutionalization was 1, 821 days (95% confidence interval [CI]: 1, 539-1, 981 days) for the care-recipients of employed caregivers and 1, 542 days (95% CI: 1, 284-1, 653 days) for the care-recipients of unemployed caregivers (p = 0.0634). The adjusted acceleration factor was 1.85 (95% CI: 1.08-3.86), controlling for caregiver thoughts about institutionalizing the care-recipient, caregiver health, and the use of a day center to help provide care. Conclusions - For the care-recipients of employed caregivers, the adjusted time to institutionalization was longer than for the care- recipients of unemployed caregivers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".