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Record W2331819738 · doi:10.3928/01484834-20110317-02

A Service-Learning Experience to Teach Baccalaureate Nursing Students About Health Policy

2011· article· en· W2331819738 on OpenAlexaffabout
Catherine O'Brien-Larivée

Bibliographic record

VenueJournal of Nursing Education · 2011
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsService-learningMandateExperiential learningHealth promotionCharterNursingMedicineHealth policyPublic healthMedical educationPromotion (chess)Nurse educationPopulationService (business)PsychologyPedagogyPolitical scienceBusiness

Abstract

fetched live from OpenAlex

Incorporating health promotion strategies in practice, and in particular within healthy public policy based on the Ottawa Charter, is widely recognized as within the mandate of nursing, although evidence suggests that nurses are reluctant to take on this role. An innovative strategy was developed to facilitate baccalaureate nursing students' learning about healthy public policy by immersing them in a real-world service-learning experience. Students partnered with a population, assessed the determinants of health, and implemented a population health promotion strategy that included attention to a health policy issue. Students identified strengths and weaknesses of the existing policy and were required to propose recommendations for change that addressed the social justice issues. Students presented their work to faculty, students, and community partners and developed a written position paper on the topic. Students evaluated the service-learning experience as an excellent experiential learning opportunity.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0020.010
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0220.009

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 head0.073
GPT teacher head0.445
Teacher spread0.372 · 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 designNot applicable
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

Citations34
Published2011
Admission routes2
Has abstractyes

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