Bibliographic record
Abstract
Extensive small scale studies have documented that when people assume the role of assisting a person with impairments or an older person, care activities account for a significant portion of their daily routines. Nevertheless, little research has investigated the problem of measuring the time that carers spend in care-related activities. This paper contrasts two different measures of care time – an estimated average weekly hours question in the 1998 Australian Survey of Disability, Ageing and Carers, and diary estimates from the 1997 national Australian Time Use Survey. This study finds that diaries provide information for a more robust estimate, but only after one models the time use patterns in the days of carers to identify care-related activities, which diarists do not necessarily record as care. Such a measure of care time reveals that even people who offer only occasional assistance to a person with impairments tend to spend the equivalent of more than 10 minutes a day providing care. Most caregivers undertake the equivalent of a part-time job to help a friend or family member. Summing the average caregiving time provided by all household members reveals that over a quarter of Australian households caring for an adult or child provide the equivalent of a full-time employee's labour, and another quarter work between 20 and 39 total weekly hours to provide informal care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".