Everyday experiences of implicit rationing: comparing the voices of nurses in California and British Columbia
Bibliographic record
Abstract
Managed Care in the U.S. is one of the more controversial strategies to implicitly ration health care. It has also been creeping into Canada where care is similarly being managed albeit in a different socio‐political environment. Based on nine group interviews with 35 RNs in California and 10 group interviews with 39 RNs in British Columbia, we find that the price to be paid for the promise of cheaper, more efficient health care through managerial strategies is borne largely by nurses and other health care providers. The data reveal that nurses in British Columbia and California share similar experiences with how the amount of care is rationed at the bedside – through care pathways, early discharge policies and reduced staffing – while the rationing of access to care differs because of the socio‐political contexts of their respective health care systems. In both cases, the implicit rationing of care through managerial strategies fails to deliver on its promises.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".