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Record W4246782539 · doi:10.32920/ryerson.14647986.v1

Factors associated with the extent of information technology use in Ontario hospitals

2021· preprint· en· W4246782539 on OpenAlexaffabout
Catherine Ka Yan Chow

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsSAFERHealth careMedical emergencyBusinessMedicineEnvironmental healthGeographyEconomic growthComputer science

Abstract

fetched live from OpenAlex

Patients, providers and society are demanding more from health care systems worldwide. As health systems evolve, the use of health information technology is one method to deliver safer, more efficient, and more effective patient care. This paper presents analysis that explores whether location, hospital type, hospital size are factors in determing the extent that IT is used in Ontario hospitals.The results show that urban hospitals use IT more extensively than non-urban hospitals. Hospital type does not have an effect on the relative extent that IT is used. Larger hospitals are likely to use IT more than smaller hospitals. Key implications for having location and size determine a hospital's use of IT are the increasing divide between urban and non-urban hospitals and the proliferation of smaller "have not" hospitals in Ontario.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.233
GPT teacher head0.399
Teacher spread0.167 · 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.

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
Published2021
Admission routes2
Has abstractyes

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