Access to Childcare\nand Home Care\nServices across\nEurope. An Analysis of the European Union Statistics on Income and Living Conditions (EU SILC), 2016. Social Inclusion\nReport No 8. September 2019
Bibliographic record
Abstract
This report uses EU-SILC data for 2016 to examine differences by social risk group and social class in access to care services – specifically, childcare and home care for people with an illness or disability. We focus on 11 countries and four welfare regimes across Europe. We also examine the association between access to these services and both poverty and employment. There are three main findings. First, countries with universal services, or a strong welfare state, provide greater access to care overall, and greater access for vulnerable social risk and social class groups. Countries with means tested services offer lower coverage which results in a greater chance of unmet need for care. Second, certain social-risk groups have a higher chance of experiencing unmet need for childcare and home care. Social class or household composition differences within such groups cannot fully explain their likelihood of reporting unmet need. This suggests that social-risk groups are particularly vulnerable to unmet need. Third, unmet need for childcare and home care is associated with deprivation and, in the case of childcare, non-employment. In this way, unmet need for childcare in particular may act as a barrier to labour market participation. Although our analysis cannot establish a causal link between the two, unmet care need and non-employment are related, and could be a significant force for social exclusion. Policy efforts should limit the experience of unmet care needs.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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".