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Record W4408779839 · doi:10.32920/28651901.v1

Professional Practice in Nursing: Part II

2025· preprint· en· W4408779839 on OpenAlexaboutno aff
Kateryna Metersky, Roya Haghiri‐Vijeh, Jasmine Balakumaran, Oona St-Amant, Leigh Dybenko, Emilene Reisdorfer, Linda Scott, Anita Jennings

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsNursingNursing practicePsychologyMedicine

Abstract

fetched live from OpenAlex

Professional Practice in Nursing: Part II Case Studies provides educators and students with weekly case studies on nursing concepts related to acute and life threatening illnesses. The weekly case studies provide students with realistic case scenarios of clients as they navigate the various levels and settings of the healthcare system with acute and life-threatening health conditions. Each case study provides a series of critical thinking questions for students to reflect on their roles as nurses in providing care within the Canadian healthcare system through using a reflective inquiry, reflective practice and critical social theory lenses. The intention of these case studies is to help students continue to develop their clinical judgment and reasoning skills.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0240.008

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 head0.045
GPT teacher head0.412
Teacher spread0.366 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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