Healthcare Restructuring with a View to Equity and Efficiency: Reflections on Unintended Consequences
Bibliographic record
Abstract
This paper is developed from a research study that examined the hospitalization and helpseeking experiences of diverse ethnocultural populations in the era of healthcare restraint. Interview data were gathered from 60 patients while hospitalized and after their discharge home. Fifty-six healthcare professionals, the majority of whom were nurses caring for these patients while they were in hospital, were also interviewed. The data gathered in this study provides evidence to illustrate how restructuring associated with fiscal restraint designed to enhance efficiencies while ensuring the provision of medically necessary services, has had unintended consequences for some groups of patients and for nurses. These consequences have created a context for inequities in care delivery for those most vulnerable. In this paper we trace the ways in which the changed context of care delivery has exerted its effects on both nurses and patients and illustrate how each has sought to bridge gaps created when organizational supports are lacking. Our study data offer insight into the complexities of the practice setting and difficulties that arise when resources cannot be mobilized to match patients' needs. Our analysis examines how tensions between ideologies of efficiency and accessibility are navigated at the front lines, and draws attention to unintended consequences of the current policy context.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.069 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.010 |
| 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".