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Record W4213363441 · doi:10.17483/2368-6669.1287

Nurse Educators’ Perceptions of Ecoliteracy in Undergraduate Nursing Education

2022· article· en· W4213363441 on OpenAlexaffvenueabout
Jennifer Lynn Morin, Benita Cohen, Nicole Harder, Shirley Thompson

Bibliographic record

VenueQuality Advancement in Nursing Education - Avancées en formation infirmière · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of ManitobaRed River College
Fundersnot available
KeywordsNurse educationNursingCurriculumIndigenousHealth careMedicinePsychologyPolitical scienceEcologyPedagogy

Abstract

fetched live from OpenAlex

Introduction: Increased human health concerns related to the natural environment and climate change are having a growing impact on nursing practice. This past year in Canada, for example, British Columbia reported the highest number of heat related deaths, followed by devastating forest fires in many provinces that have disproportionately impacted Indigenous Peoples. Nurses are well positioned to address the direct health impacts associated with climate change. As a result, nurses require an increased level of ecoliteracy to address the health impacts linked to climate change. The role of the nurse in addressing the health impacts of climate change are vast, ranging from direct patient care, education and advocacy, their role includes supporting individuals, communities, and populations to mitigate, adapt and build resiliency in the face of a changing climate. Background: Regulatory and professional associations support the professional emphasis on the significance of ecoliteracy yet there is insufficient understanding of the resistance to the content in nursing curricula. Although current literature supports an increased emphasis on the integration of ecosystem health concerns and the impact on human health within undergraduate nursing education, there is a paucity of empirical evidence regarding nursing educators’ perspectives on the subject. This study is a first step in gaining a greater understanding of the perspectives of nurse educators on ecoliteracy within undergraduate nursing education programs in one Canadian province. Methods: This qualitative research study included 13 nurse educators from three diverse academic settings. Data were collected using semi-structured, open-ended interview questions, followed by content analysis of the data. Results: Data analysis revealed five key themes: a) importance of ecoliteracy in undergraduate nursing programs; b) current integration of ecoliteracy concepts in curricula; c) future considerations for ecoliteracy content; d) barriers to the inclusion of content supportive of ecoliteracy in curricula; e) strategies to address barriers. While educators feel that ecoliteracy is important in undergraduate nursing, they noted that the current integration of climate content in the curriculum is uneven. Barriers and potential strategies to integrating this content are identified. Conclusion: The findings of this study can be used for curriculum revision and to stimulate innovation and research in nursing education. This study creates the opportunity for a larger scale replication study, pilot studies of the integration of concepts that would support ecoliteracy, and further research on the topic. This study identified that many complexities are involved in achieving ecoliteracy in nursing education and suggest that while threading of content can address the urgent need, further research is required to identify entry to practice requirements for undergraduate nursing programs.

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)0.006
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.002
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 head0.036
GPT teacher head0.415
Teacher spread0.379 · 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

Citations8
Published2022
Admission routes3
Has abstractyes

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