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Record W1973450227 · doi:10.2202/1548-923x.1057

Outcomes of Master's Education in Nursing

2004· article· en· W1973450227 on OpenAlexaffabout
Catherine E. Cragg, Mary‐Anne Andrusyszyn

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2004
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsWestern UniversityUniversity of Ottawa
Fundersnot available
KeywordsNursingCredibilityPrideNurse educationTheme (computing)Health careMedicinePerspective (graphical)PsychologyQualitative researchMedical educationSociology

Abstract

fetched live from OpenAlex

This qualitative, descriptive study examined changes in perspective experienced by 22 recent graduates of Master's in Nursing programs from three Ontario universities. Participants responded to semi-structured, taped interviews and recounted personal, practice, and attitudinal changes they could attribute to completing a Master's program in Nursing. Among outcomes were personal ones including greater self-confidence, credibility, and acuity of critical thinking. Deeper and broader world-views of the profession and health care were evident. All participants valued evidence-based practice, reporting greater use of multiple information sources. Participants communicated with other professionals on more equal terms and some described a multicultural function, translating knowledge between staff nurses and members of other disciplines. Pride in nursing and its potential for shaping health care was a recurring theme. More professional opportunities became available following Master's studies. The outcomes of this study contribute to understanding the effects of graduate education 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)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.050
GPT teacher head0.428
Teacher spread0.378 · 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 designObservational
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

Citations19
Published2004
Admission routes2
Has abstractyes

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