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Record W3177239561 · doi:10.1097/nnd.0000000000000738

Expedited Cross-Training

2021· article· en· W3177239561 on OpenAlex
S. Patel, Benjamin Hartung, Roxana Nagra, Amy Davignon, Taranvir Dayal, Maria Nelson

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.

Bibliographic record

VenueJournal for Nurses in Professional Development · 2021
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsLawson Health Research InstituteCARE CanadaRegistered Nurses' Association of OntarioBaycrest HospitalNOSM UniversitySystems, Applications & Products in Data Processing (Canada)
Fundersnot available
KeywordsStaffingEconomic shortageTraining (meteorology)NursingPlan (archaeology)Professional developmentMedicineMedical education

Abstract

fetched live from OpenAlex

Cross-training of nurses is an approach used by hospitals to mitigate anticipated nurse staffing shortages. This article provides professional practice nurse educators guidance on how to plan, implement, and evaluate expedited cross-training that integrate the principles of just-in-time training. Sixty-one nurses in a postacute care hospital setting were cross-trained over the course of 8 weeks using a six-step method.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.052
GPT teacher head0.425
Teacher spread0.373 · 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