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Record W2400812205 · doi:10.1177/107937390602900101

What's behind the Data: An Examination of the Processes and Policies Underlying the Routine Collection of Clinical Data in Ontario Hospitals

2006· article· en· W2400812205 on OpenAlex
Paula Blackstein-Hirsch, Ruth Croxford, Virginia Flintoft, Adalsteinn Brown

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Health and Human Services Administration · 2006
Typearticle
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsData collectionPsychologyMedical emergencyMedicineSociology

Abstract

fetched live from OpenAlex

This article surveyed the processes and policies underlying the routine collection of clinical data in acute care hospitals in Ontario, Canada. Although there is evidence of a small shortfall in the availability of human resources, most health records departments employ experienced staff with health records certification. However, there is much more important variation in the documented and undocumented processes used to generate routinely collected clinical data. Current guidelines and coding schedules are helpful but insufficient to guide the production of good quality data. The variations in the processes used to produce clinical data have important implications for the management, reimbursement, and planning of healthcare. This is particularly critical at a time when hospitals and other stakeholders, such as governments, are relying more and more on accurate, reliable, and comparable 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.

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)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.434
GPT teacher head0.533
Teacher spread0.099 · 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