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Record W2787611802 · doi:10.5430/jnep.v8n7p31

Evaluating the social-learning environment of a regional men in nursing conference

2018· article· en· W2787611802 on OpenAlexvenueno aff
Jennifer Harrison, Guy Beck, Adam Voegele, William T. Lecher, Gordon Lee Gillespie

Bibliographic record

VenueJournal of Nursing Education and Practice · 2018
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
FundersUniversity of Cincinnati
KeywordsNursingQualitative researchExploratory researchContent analysisPsychologyNurse educationMedicineSociology

Abstract

fetched live from OpenAlex

Barriers and challenges such as the lack of role models exist for men in nursing. Efforts are needed to provide opportunities for men in nursing to discuss, engage, and network with other men about issues of importance to them. To address this need, a regional conference for men in nursing was held. The utility of this conference specific for men in nursing was evaluated for its ability to provide a space and forum dedicated to men in nursing for socializing and learning with other men in nursing. An exploratory qualitative design was used to examine the experiences of conference attendees. Respondents (n = 62) anonymously completed a program evaluation tool. The qualitative data were analyzed using a constant comparative analysis method. Five themes were derived from the qualitative data: Conference Logistics, Effectiveness of Presenters, Key Messages from the Presentations, Men in Nursing, and Challenges Men in Nursing Face. Future conferences need to incorporate more clinically-oriented topics with speakers specifically discussing the importance of their content for men in nursing.

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)0.038
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0070.003
Open science0.0030.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.376
GPT teacher head0.616
Teacher spread0.240 · 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 designQualitative
Domainnot available
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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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