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Record W4382723035 · doi:10.24193/tras.69e.5

"Assessment of the Health System Performance in Ontario Major Cities (Canada)"

2023· article· en· W4382723035 on OpenAlexaffabout
Adela P. Nistor, Diana-Gabriela Reianu

Bibliographic record

VenueTransylvanian Review of Administrative Sciences · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsPublic healthGovernment (linguistics)Health careBenchmarkingBusinessMedicineHealth policyPopulationEnvironmental healthNursingEconomic growthEconomics

Abstract

fetched live from OpenAlex

"This study aims to provide evidence to support policymaking by assessing health system perfor mance in the province of Ontario, Canada, and its major cities (Toronto, Mississauga, Oakville, and Brampton). It reports on the performance of health care organizations and local health systems over the years 2012-2018. The performance indicators ana lyzed are grouped into two categories: health status and quality of service (Ontario Ministry of Health). The analysis reports health care waiting times for the year 2018, focusing on the most frequently re ported disease groups and procedures such as: pediatric, cancer, cardiac, orthopedic, eye, diagnos tic imaging, and emergency room. It also looks at government spending on health, the benchmark for surgical procedures, the number of physicians, the health status of the population, and rates of hospi talization and hospital admissions. As recommen dations, among policies to improve the health sys tem, the government should pay attention to health spending, increasing the number of doctors rather than beds, and introducing the privately-owned hos pitals that could coexist with the public ones."

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.132
GPT teacher head0.346
Teacher spread0.214 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes2
Has abstractyes

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