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Record W1999393802 · doi:10.1504/ijiq.2008.022958

The National Ambulatory Care Reporting System: factors that affect the quality of its emergency data

2008· article· en· W1999393802 on OpenAlexaffabout
Debbie Gibson, Heather Richards, Ann Chapman

Bibliographic record

VenueInternational Journal of Information Quality · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsCanadian Institute for Health Information
Fundersnot available
KeywordsData collectionMedical emergencyData qualityEmergency departmentAffect (linguistics)Quality (philosophy)Activity-based costingAmbulatory careOperations managementMedicineComputer scienceBusinessHealth careNursingPsychologyEngineeringStatistics

Abstract

fetched live from OpenAlex

The National Ambulatory Care Reporting System (NACRS) records details of all of Ontario's emergency department visits. It is used for several planning, reporting and research purposes including facility-specific utilisation management decisions, costing and clinical outcomes research, policy development, and system planning and evaluation. Uses of NACRS data for these purposes rely on the data being both complete and accurate. Our goal was to determine the factors that affect the collection of high-quality NACRS emergency department data. The study involved surveying 15 facilities sampled from Ontario for a reabstraction study and completion of a self-administered questionnaire. The reabstraction study was designed to compare data obtained via the study to original data included on the NACRS database. The questionnaire collected information on facility practices. Study facilities were grouped by questionnaire responses, and comparisons of data discrepancy and agreement rates between groups highlighted the relationship between facility practices and data accuracy.

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)0.009
metaresearch head score (Gemma)0.005
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 score0.711
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.394
GPT teacher head0.435
Teacher spread0.040 · 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

Citations54
Published2008
Admission routes2
Has abstractyes

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