MétaCan
Menu
Back to cohort
Record W4206315289 · doi:10.28984/drhj.v5i2.344

Undergraduate Nursing Students' Experience of Northern Rural and Remote Indigenous Communities

2022· article· en· W4206315289 on OpenAlexaffvenue
B Sc, Beverly Breen, Joanne Carbonneau RN B.Sc.N.

Bibliographic record

VenueDiversity of Research in Health Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsNorthern CollegeLaurentian University
Fundersnot available
KeywordsIndigenousPraxisNursingNurse educationCurriculumMedical educationMedicineSociologyPedagogyPsychologyPolitical science

Abstract

fetched live from OpenAlex

Undergraduate nursing programs are moving towards a service learning model in teaching nursing student cultural awareness. In this article, we discuss the nursing student experience in a university elective which immerses students in rural and remote Indigenous communities resulting in cultural consciousness. This service learning experience that students encountered promoted growth in nursing praxis, and fostered positive curriculum growth and community partnerships between the College and the Indigenous communities in which they visited. Students gained cultural consciousness and increased awareness, which is beneficial in their future nursing careers as they grow into better culturally competent care providers. Also discussed is the history and background of these Indigenous communities, The Truth and Reconciliation Commission (TRC) and the First Nations Principles of OCAP (ownership, control, access and possession). These topics are discussed in detail throughout the student experience as they respond to nurses’ professional standards, development of cultural competency and integrating calls to action in truth and reconciliation.

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)0.003
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 score0.993
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0080.003
Scholarly communication0.0040.001
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.176
GPT teacher head0.465
Teacher spread0.290 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueDiversity of Research in Health JournalSame topicService-Learning and Community EngagementFrench-language works237,207