Measurement of informal care time in a study of patients with dementia
Bibliographic record
Abstract
BACKGROUND: Previous assessments of informal care time have tended to consider only the amount of time spent with the patient by the primary informal caregiver; however, in many cases, more than one person is providing care for the patient. We assess total informal care time of people caring for patients with dementia, and estimate the bias that can arise if consideration is not made of the time spent by all participating informal caregivers. METHOD: We used an extended version of the questions on informal care time from the Resource Utilization in Dementia (RUD) instrument. Caregivers were asked to state the number of days and the number of hours on a typical day they had assisted the patient in activities of daily living (ADL), instrumental ADL (IADL), and supervision during the last four weeks. Multivariate regression analyses were conducted to identify factors that could account for the amount of informal care time. RESULTS: 357 informal caregivers took part. Values were missing from only 4.5% of all interviews. On average, the primary informal caregiver cared for the patient 1.5, 2.1 and 1.9 hours per day in ADL, IADL and supervision respectively. Fifty-seven percent of all patients had more than one informal caregiver. Total informal care time was underestimated by about 14% if the time of caregivers other than the primary caregiver was not taken into account. The informal care time was significantly higher if the caregiver was the patient's partner and the patient's health status was lower. CONCLUSION: Our results show that most previous studies probably underestimated costs of informal care because the time of informal caregivers other than the primary caregiver was not considered.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".