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Record W4361190420 · doi:10.1111/nin.12550

Who dismantled nursing mentorship systems and how do we get them back?

2023· editorial· en· W4361190420 on OpenAlexaff
Sally Thorne

Bibliographic record

VenueNursing Inquiry · 2023
Typeeditorial
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMentorshipNursingCompetence (human resources)Nurse educationContext (archaeology)MedicinePsychologyMedical education

Abstract

fetched live from OpenAlex

Who dismantled nursing mentorship systems and how do we get them back?In most professions, it is well understood that new graduates are only beginning their integration of the complexity of knowledge, skills, and know-how that will ultimately allow them to practice to the full scope of their capacity.Close engagement with more senior professional mentors is recognized as requisite to achieving their full potential.However, all too often in these modern times, formal mentorship only appears in the specific contexts of preregistration nursing students and very new graduates.

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)0.015
metaresearch head score (Gemma)0.049
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.035
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.049
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0040.003
Science and technology studies0.0080.008
Scholarly communication0.0170.011
Open science0.0060.002
Research integrity0.0350.042
Insufficient payload (model declined to judge)0.0050.008

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 head0.058
GPT teacher head0.340
Teacher spread0.283 · 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
GenreEditorial

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

Citations1
Published2023
Admission routes1
Has abstractyes

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