The nature of informal caregiving for medically ill older people with and without depression
Bibliographic record
Abstract
OBJECTIVES: To describe patient and caregiver perceptions of the nature of informal caregiving in a sample of older medical inpatients with and without depression. METHODS: One hundred and fifty-four patient-caregiver pairs were recruited from a larger prospective observational study of three groups of medical inpatients aged 65 and over, with major, minor, and no depression, respectively, and with at most mild cognitive impairment. Interviews were conducted at the time of hospital admission to assess characteristics of patients (disability, comorbidity, perceptions of support) and caregivers (relationship, residence, types of assistance and time spent caregiving). Time spent on the physical tasks of caregiving (assistance with activities of daily living, physical care, transport) was estimated by all caregivers. Time spent on emotional or other support was estimated only for non-coresident caregivers RESULTS: In multivariable analyses, neither major nor minor depression was associated with time spent on physical support; major depression was associated with significantly increased time spent by non-coresident caregivers on emotional or other support; minor depression was associated with perceived inadequacy of support. CONCLUSIONS: Major depression is independently associated with greater time spent by non-coresident caregivers on emotional or other support; minor depression is associated with perceived inadequacy of support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".