Cutting from the Bottom: Reform and Hospital Work Under the Influence of New Public Management
Bibliographic record
Abstract
Health care research predominantly analyzes the role of pharmaceuticals and physicians. Less focus is given to the majority of workers who are essential to the health care system. While often ignored, the non-physician work force has a large impact on health care financing and health care outcomes. Their work and the context they work in needs to be analyzed to provide a fuller understanding of health care systems. This dissertation is an attempt to fill this gap in health research. Hospital work exists in a complicated political context. Hospitals in Canada are privately operated, non-profit corporations that are publicly funded directly by provincial governments and indirectly by the federal government. Both levels of government have attempted to constrain health care spending in the last few decades, and much of this reduction was aimed at non-physician hospital workers and services. This dissertation utilized theories from public administration and sociology to contextualize hospital work within large political shifts and how they have impacted hospital work. These theories guided the literature review, the interview schedule, and the analysis and interpretation of the data. This dissertation examined the impact of health care reform on hospital work. Semi-structured interviews were utilized to ask hospital workers about their work, work-life balance, and how hospital work has changed over their careers. In total, 20 hospital workers were interviewed, including 10 nurses and 10 non-nurse workers. Interviews ranged from 30 to 65 minutes. Most respondents were from hospitals downtown Toronto, with the remaining working in hospitals within the Greater Toronto Area. This dissertation found that successive waves of health care reform have negatively impacted hospital work. Workers indicated various ways that the changing political and economic climate has impacted their lives and their ability to perform their work at a high level. In general, workers cited lowered autonomy, increased automation and documentation, and increased managerial control over their work. Workers also reported work intensification and high rates of injury. Workers pointed to budget cuts and a shift in accountability down to the workers to explain why these changes had occurred.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".