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Collaborating to promote psychiatric mental health nursing research

2019· article· en· W2914742312 on OpenAlexaff
Chantille Haynes, Joy Maddigan

Bibliographic record

VenueNursing Management · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMental healthNursingMental health nursingAddictionPsychologyMEDLINENursing researchMedicineMedical educationPsychiatry

Abstract

fetched live from OpenAlex

In Brief The collaboration of clinical nurses and academic nurse researchers is required to determine best practice. The development of a collaborative between a mid-sized university's nursing faculty and a clinical mental health and addictions program shows how to engage nurses in research to create an evidence-based culture.

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)0.243
metaresearch head score (Gemma)0.222
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.757
Threshold uncertainty score0.933

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2430.222
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0070.004
Science and technology studies0.0210.013
Scholarly communication0.0310.021
Open science0.0070.064
Research integrity0.0230.039
Insufficient payload (model declined to judge)0.0200.018

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 head0.155
GPT teacher head0.584
Teacher spread0.429 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreCommentary

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
Published2019
Admission routes1
Has abstractyes

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