A Systematic Review of the Impact of Healthcare Reforms on Access to Emergency Department and Elective Surgery Services
Bibliographic record
Abstract
This systematic review sought to identify whether health care reforms led to improvement in the emergency department (ED) length of stay (LOS) and elective surgery (ES) access in Australia, Canada, New Zealand, and the United Kingdom. The review was registered in the PROSPERO database (CRD42015016343), and nine databases were searched for peer-reviewed, English-language reports published between 1994 and 2014. We also searched relevant "grey" literature and websites. Included studies were checked for cited and citing papers. Primary studies corresponding to national and provincial ED and ES reforms in the four countries were considered. Only studies from Australia and the United Kingdom were eventually included, as no studies from the other two countries met the inclusion criteria. The reviewers involved in the study extracted the data independently using standardized forms. Studies were assessed for quality, and a narrative synthesis approach was taken to analyze the extracted data. The introduction of health care reforms in the form of time-based ED and ES targets led to improvement in ED LOS and ES access. However, the introduction of targets resulted in unintended consequences, such as increased pressure on clinicians and, in certain instances, manipulation of performance data.
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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 | 0.015 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".