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Record W1849585007 · doi:10.3928/01484834-20040101-05

Baccalaureate Nursing Students’ Attitudes Toward Poverty: Implications for Nursing Curricula

2004· article· en· W1849585007 on OpenAlexaffabout
Wendy Sword, Linda Reutter, Donna Meagher‐Stewart, Elizabeth Rideout

Bibliographic record

VenueJournal of Nursing Education · 2004
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPovertyCurriculumNursingFace (sociological concept)Nurse educationPsychologyInterpersonal communicationHealth careMedicinePedagogySociologyPolitical scienceSocial psychologySocial science

Abstract

fetched live from OpenAlex

Given the link between poverty and health, nurses, in their work in hospitals and in the community, often come into contact with people who are poor. To be effective care providers, nurses must have an adequate understanding of poverty and a positive attitude toward people who are poor. This study examined attitudes toward poverty among baccalaureate nursing students (N = 740) at three Canadian universities. Students' attitudes were neutral to slightly positive. Personal experiences appeared to have an important influence on the development of favorable attitudes. The findings point to several considerations for nursing curricula. Students should not only be provided with classroom opportunities for critical exploration of poverty and its negative effects on individuals and society, but also have clinical learning experiences that bring them face-to-face with people who are poor, their health concerns, and the realities of their circumstances. Thoughtful critique of poverty-related issues and interpersonal contact may be effective strategies to foster attitude change.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.825

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.259
GPT teacher head0.580
Teacher spread0.321 · 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

Citations36
Published2004
Admission routes2
Has abstractyes

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