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Record W3129093179 · doi:10.1016/j.nepr.2021.102980

Planting seeds of community-engaged pedagogy: Community health nursing practice in an intergenerational campus-community gardening program

2021· article· en· W3129093179 on OpenAlexafffund
Sonya L. Jakubec, Joanna Szabo, Judy Gleeson, Genevieve Currie, Sonya Flessati

Bibliographic record

VenueNurse Education in Practice · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsMount Royal University
FundersMount Royal UniversityTD Friends of the Environment Foundation
KeywordsParticipatory action researchPedagogyFocus groupInclusion (mineral)Neighbourhood (mathematics)Citizen journalismCommunity-based participatory researchSociologyNursingMedical educationMedicinePolitical scienceGender studies

Abstract

fetched live from OpenAlex

As part of a participatory action research (PAR) study, nursing student participants collaborated with faculty, along with older adults, people with mixed abilities, and preschool aged children in order to 'sow the seeds of social change' and grow a campus community gardening project. The focus of this article is on the community-engaged pedagogy within a community health nursing practice course that supported student learning. Insights were gleaned over the course of four academic semesters (and four student cohort groups) with students as co-developers of the campus-community garden and participants in the PAR. Key themes emerged from student participants in the PAR process including: (1) planning in community to "think global, act local"; (2) discovering 'the people in your neighbourhood' as socially just partnerships; (3) revisiting landscapes of social inclusion; and (4) reflecting on "humble togetherness" across generational gaps. The findings showcased here attest to how community-engaged pedagogy, in conjunction with PAR, can facilitate student learning outside of traditional settings and grow social inclusion, intergenerational connection, and social justice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.400
Teacher spread0.357 · 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 teacher head, not a consensus.

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

Citations24
Published2021
Admission routes2
Has abstractyes

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