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Record W4224312630 · doi:10.5430/jnep.v12n8p31

Faculty role in support of student scholary dissemination

2022· article· en· W4224312630 on OpenAlexvenueno aff
Denise Smart, Connie Kim Yen Nguyen-Truong, Deborah U. Eti, Natasha Barrow, Anne M. Mason, Gail Oneal

Bibliographic record

VenueJournal of Nursing Education and Practice · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsnot available
Fundersnot available
KeywordsMentorshipScholarshipCompetence (human resources)Medical educationPsychologyPedagogyMedicinePolitical science

Abstract

fetched live from OpenAlex

Background: Academic universities across all countries hold faculty accountable to some degree for scholarship/dissemination, teaching, and research. In concert with these expectations, student-faculty scholarly collaborations present with challenges, barriers, opportunities, and benefits to and for both parties. Time requirements, student and faculty writing skills, communication of expectations, and ethical considerations for engagement and authorship are common themes noted in the literature.Purpose: This paper’s perspective explores the intricacies of the nurse faculty role in support of undergraduate and graduate nursing student scholarly writing with an emphasis on dissemination in peer-reviewed journals.Findings: Nursing literature addresses the need for scholarly writing and dissemination around the following areas: general writing needs and academic requirements, strategies to advance student or faculty writing competence, nursing program specific challenges, institutional support, faculty productivity focused articles, writing for publication specific articles and student-faculty collaboration articles.Conclusions: Scholarship collaboration in the form of student-faculty partnerships can be a rewarding experience. Faculty benefit from forming solid and often lasting relationships, and students benefit from mentorship and satisfaction of seeing their academic work as contributing to the science of nursing. When these opportunities are acknowledged and planned out with clear role expectations and guidelines in place, faculty gain additional experience in manuscript development and students gain solid writing skills that come from practice.

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)0.040
metaresearch head score (Gemma)0.180
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.180
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0080.004
Scholarly communication0.0160.007
Open science0.0030.012
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0670.028

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 head0.092
GPT teacher head0.437
Teacher spread0.345 · 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.

Study designNot applicable
DomainReporting
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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