Ariel Ducey, Never Good Enough: Health Care Workers and the False Promise of Job Training
Bibliographic record
Abstract
H ealth care reform and restructuring are part of political discourse and policy initiatives worldwide.Commonly, reform is aimed at efficiency, making better use of existing resources in order to contain costs and to maximize quality of care.Although the importance of striking a balance between the cost and quality of care is generally agreed upon, how to accomplish this at local and national levels is highly debated.In a novel approach, Ariel Ducey looks at one dimension of health care reform, worker education.The reciprocal impact of the job training industry on health care work and workers is the main focus of Never Good Enough: Health Care Workers and the False Promise of Job Training.Ducey provides an in-depth look at both the promises and inadequacies of educational training on the economic livelihood, career mobility, and working conditions of frontline healthcare workers in New York City.Beginning in the 1990s, pro-market health care reform in New York advocated free market competition, encouraged entrepreneurship within the education industry, and stressed health system efficiency through principles of autonomy and individualism.The book is based on field work and in-depth interviews with health care workers, hospital and union officials, program planners, and educational trainers at several non-profit private healthcare facilities in New York between 1999 and 2003.Ducey's main argument is that the pro-market approach failed to address key structural issues related to health system reform.Job training initiatives were originally designed to address expectations of widespread hospital closures coupled with massive staff unemployment.These consequences did not materialize in the "unique political climate" of New York City; instead, as elsewhere in the US and other countries, unforeseen worker shortages dominated the policy and political agenda.Ducey contends that the money dedicated to job training programs did little to compensate for daily workplace challenges such as the lack of material
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".