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Record W2076934285 · doi:10.12927/cjnl.2012.22959

Measuring Outcomes of Nursing Care, Improving the Health of Canadians: NNQR (C), C-HOBIC and NQuiRE

2012· review· en· W2076934285 on OpenAlex
Susan VanDeVelde‐Coke, Diane Doran, Doris Grinspun, Laureen Hayes, Anne Sutherland Boal, Karima Velji, Peggy White, Irmajean Bajnok, Kathryn Hannah

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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNursing leadership · 2012
Typereview
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsBaycrest HospitalCanadian Nurses Association
Fundersnot available
KeywordsNursingQuality (philosophy)Nursing researchHealth careTeam nursingNurse educationPsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

The purpose of this paper is to inform the nursing community of the extraordinary progress that the Canadian National Nursing Quality Report (NNQR(C)), the Canadian Health Outcomes for Better Information and Care (C-HOBIC) and the Nursing Quality Indicators for Reporting and Evaluation (NQuiRE) have made to date, and to share our commitment to continue working together to build a strong nursing profession that, armed with evidence, will contribute to healthier Canadians.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.599
GPT teacher head0.489
Teacher spread0.109 · 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