MétaCan
Menu
← Back to cohort
Record W7115595379 · doi:10.82396/cjcd.v7i1.3017

Despite the Barriers Men Nurses are Satisfied with Career Choices

2021· article· en· W7115595379 on OpenAlexaffabout

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2021
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDescriptive researchCareer developmentMEDLINEDescriptive statisticsAdministration (probate law)Health care

Abstract

fetched live from OpenAlex

Men remain a minority in the nursing profession. In 2005, 5.6 per cent of the nurses in Canada were men (Canadian Nurses Association [CNA], 2005); while in the United States (U.S.) men comprise about 5.8 per cent of the registered nurses (U. S. Department of Health and Human Services Health Resources and Services Administration [HRSA], 2004). Although the basis for this gender imbalance has been discussed in the literature, there is a paucity of data regarding reasons why men choose nursing as a career, perceived barriers experienced in practice, and factors associated with career satisfaction. A descriptive design was used by the researchers to examine these questions among a group of male registered nurses (N = 250) in one Canadian province. Knowledge about reasons why men choose nursing, the barriers they experience in practice, and information about factors that impact career satisfaction may help to attract men into the nursing profession, and aid development of recruitment and retention strategies.

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)0.003
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.228
Teacher spread0.220 · 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
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland)→Same topicNursing education and management→French-language works237,207→