Quality of Life and Health Determinants of Informal Caregivers Aged 65 Years and Over
Bibliographic record
Abstract
Background: Informal caregivers' own quality of life, health status, and determinants are poorly understood despite their concerns for the health of the individuals they assist. Objectives: To compare the quality of life and the health determinants of older informal caregivers with those of older adults without caregiving responsibilities. Methods: An online survey was designed to investigate the quality of life and the health determinants of people aged 65 years and over, with a focus on informal caregivers. In addition to socio-demographic data, the number of informal caregiver and the Zarit scale of caregiver burden were collected. Quality of life (SF-12) and health determinants (access to technology and level of physical activity (IPAQ)) were assessed and compared between informal caregivers and the others. Results: A total of 111 participants (70 ± 3.83 years, 71.2% of women). The majority of respondents (91.8%) were Belgian. One third of the respondents identified themselves as informal caregiver and declared to have severe burden (61.9±15.2/88). Socio-demographic characteristics and access to technology were similar between informal caregivers and non-caregivers (p>0.05). However, informal caregivers have a lower SF-12 score in the mental score domain (44.3±10.2 vs; 50.7±7.0; p=0.004) and a lower level of physical activity (434±312 METS/min/week vs.1126±815 METS/min/week; p=0.01) than their peers. Conclusions: Informal caregivers reported lower quality of life and lower level of physical activity than their peers. Given the recognized importance of physical activity for overall health, this survey highlights the need to promote physical activity among older informal 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 | 0.003 |
| 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 | 0.001 |
| 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".