EMPLOYMENT OUTCOMES OF CAREGIVING ACROSS THE COUNTRIES: A REVIEW AND ANALYSIS
Bibliographic record
Abstract
There is general consensus that informal caregiving results in negative outcomes on caregivers’ employment. While a rich body of research has investigated the differences in the outcomes in terms of gender, race, and income, there is scant research that explain differences at a macro level such as structural arrangements that may enhance or mitigate these outcomes. This study asks whether cross-national variation in patterns of employment outcomes of informal caregiving can be explained by national demographic and socioeconomic differences between individuals, or whether structural and cultural factors are more important. Studies were limited to original quantitative research, written in English and published from January 2000 through December 2015. The countries examined included Australia, Canada, China, Denmark, Germany, Italy, Japan, Netherlands, Norway, Korea, Spain, Sweden, UK, and US. Results are summarized with respect to different care regimes such as family-based care model vs. de-familialised, and with respect to different regions such as European, North American, and Asian countries. Macro-level factors were significant in attenuating the effects of caregiving on employment-related outcomes. Findings suggest clustering eldercare models helps to focus on some important aspects and identify similarities and differences among countries in terms of the outcomes of informal caregiving. Cultural and institutional differences might explain the differences among countries. Different systems of eldercare have been shaped over time by a complex array of historical, cultural, social and economic factors. Many of these factors are not directly part of care systems but nonetheless have important implications for different caring regimes.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".