Nurses’ Labor Conditions, Gender, and the Value of Care Work in Post-Neoliberal El Salvador
Bibliographic record
Abstract
Neoliberal cut-backs in health-care spending have had numerous negative impacts on nurses, but we know less about how they fare when governments move from neoliberal austerity to reinvestment in their health-care systems. El Salvador is an apt case to examine for how a post-neoliberal health-care reform, launched in 2010 by the newly elected FMLN government, addresses the deterioration in nurses’ work conditions caused by austerity policies. Based mainly on focus groups, interviews and participant observation conducted in the first three years of the reform’s implementation, the analysis finds important strides for nurses, especially in increased hiring in the expanded components of public health-care, and the reduction of labor precarity in formal employment. But several problems continue to imperil nurses’ well-being, reflecting, in part, a persistent devaluation of the care work that is performed mainly by women.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".