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Record W3193408211 · doi:10.1136/ebnurs-2020-103385

Targeting personalised leadership factors based on the organisational needs of nurses may cultivate and improve their nursing leadership

2021· letter· en· W3193408211 on OpenAlexaff
Shaminder Singh, Sumeeta Kapoor

Bibliographic record

VenueEvidence-Based Nursing · 2021
Typeletter
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsFoothills Medical CentreAlberta Health ServicesMount Royal University
Fundersnot available
KeywordsNursingHealth careLeadership developmentLeadership studiesPsychological interventionEducational leadershipShared leadershipLeadershipPsychologyLeadership styleMedicinePolitical sciencePedagogyPublic relations

Abstract

fetched live from OpenAlex

Commentary on: Cummings GG, Lee S, Tate K, et al . The essentials of nursing leadership: a systematic review of factors and educational interventions influencing nursing leadership. Int J Nurs Stud 2020;115:103842. Nursing leadership transpires clinical, administrative and executive roles and responsibilities of nurses across various settings such as academic institutions, community and hospitals.1 Yet, what factors constitute nursing leadership and how to practically institute and improve the leadership practices in healthcare settings are persistent scholarly questions,2 which Cummings et al strove …

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.163
GPT teacher head0.311
Teacher spread0.148 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
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
Published2021
Admission routes1
Has abstractyes

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