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Record W4283731574 · doi:10.1097/nnd.0000000000000815

Overcoming the Disruption of Clinical Nursing Education

2022· article· en· W4283731574 on OpenAlex
Joan Insalaco Warren, Jennifer Stephenson Zipp, Jana Goodwin, Eursula David-Sherman

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 · 2022
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsCanadian Nurses Association
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicTask forceNursingTask (project management)Nurse educatorProfessional developmentMedical educationNurse educationMEDLINE2019-20 coronavirus outbreakPsychologyNursing staffMedicinePolitical science

Abstract

fetched live from OpenAlex

Havoc of the COVID-19 pandemic on prelicensure nursing programs caused students to lose precious onsite clinical opportunities to gain competency in basic fundamental skills. A statewide task force of faculty and hospital leaders developed the Transition to Nurse Residency Program to develop new nurses' skills and behaviors routinely learned during onsite clinical experiences. This article describes the program contents and shares its contents for use by nursing professional development practitioners.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.096
GPT teacher head0.533
Teacher spread0.438 · 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