MétaCan
Menu
Back to cohort
Record W2123679698 · doi:10.5430/jnep.v2n4p56

Specialization in nursing management – distance learning in Brazil: Importance and application from the student perspective

2012· article· en· W2123679698 on OpenAlexvenueno aff
Fabiana Silva Okagawa, Elena Bohomol, Isabel Cristina Kowal Olm Cunha

Bibliographic record

VenueJournal of Nursing Education and Practice · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)IdeologyExploratory researchPoliticsIntervention (counseling)NursingNurse educationDistance educationPsychologyMedical educationSociologyPedagogyPolitical scienceMedicineSocial scienceComputer science

Abstract

fetched live from OpenAlex

Background: This study presents student views on the programmatic content relevant for professional practice, in a distance learning specialization course on Nursing Management. Methods: This was an exploratory, qualitative study done with 216 nursing students from five states in Brazil. Students' comments were classified according to a theoretical reference on the structure of nursing management knowledge endorsed by Sanna, addressing theoretical and ideological foundations, intervention methods, and resource management practices. Results: Students expressed that all subjects in the course are important, prioritizing human and political resource management practices, in addition to highlighting leadership and planning themes. The course met the students' expectations regarding both content mastery and applicability. Conclusions: Distance learning was found to be relevant in national education as a strategy for democratizing knowledge in Brazil, a country of continental dimensions.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.538
Teacher spread0.479 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Nursing Education and PracticeSame topicHealth, Nursing, Elderly CareFrench-language works237,207