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Record W2737398984 · doi:10.1177/0020731417722089

A Systematic Review of the Impact of Healthcare Reforms on Access to Emergency Department and Elective Surgery Services

2017· review· en· W2737398984 on OpenAlexaffabout
Sandeep Reddy, Peter Jones, Harsha Shanthanna, Raechel Damarell, John Wakerman

Bibliographic record

VenueInternational Journal of Health Services · 2017
Typereview
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's Hospital
Fundersnot available
KeywordsGrey literatureHealth careMedicineEmergency departmentInclusion (mineral)MEDLINESystematic reviewFamily medicinePolitical scienceNursingPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.494
Teacher spread0.411 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2017
Admission routes2
Has abstractyes

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