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Record W3098175970 · doi:10.11124/jbies-20-00039

Educators’ strategies for engaging diverse students in undergraduate nursing education programs: a scoping review protocol

2020· review· en· W3098175970 on OpenAlexaff
Damilola Iduye, Adele Vukic, Ingrid Waldron, Sheri Price, Catherine Sheffer, Shelley McKibbon, Rachel Dorey, Ziwa Yu

Bibliographic record

VenueJBI Evidence Synthesis · 2020
Typereview
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsKellogg's (Canada)Nova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsProtocol (science)Medical educationNurse educationPsychologyMedicineAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this scoping review is to identify and chart teaching strategies that educators use in classroom settings to engage diverse students in undergraduate nursing education programs. INTRODUCTION: Student engagement is critical to facilitating academic success and significant learning experiences for undergraduate nursing students. However, students from diverse backgrounds face challenges in undergraduate nursing programs, and these challenges impact their academic engagement and sense of belonging and inclusion. Creating conditions in nursing education that foster engagement by meeting the learning needs of diverse learners could facilitate their success, which ultimately might strengthen the nursing workforce diversity. INCLUSION CRITERIA: This review will consider papers on how educators engage undergraduate nursing students from diverse backgrounds in classroom settings, including online, face-to-face, and blended formats, irrespective of the country. Evidence obtained from all sources including qualitative, quantitative, and mixed methods studies, systematic reviews, as well as gray literature will be considered for inclusion. METHODS: JBI methodology for scoping reviews, which includes a three-step search strategy, will be employed. First, keywords will be identified from relevant articles in CINAHL and ERIC. Second, another search using the identified keywords and index terms across select databases will be conducted. Third, the reference lists of all identified articles will be screened for additional papers. Titles and abstracts will be screened by two independent reviewers, and then followed by the full text review of included articles against the inclusion criteria by two independent reviewers. Data will be extracted from included articles and the findings will be presented in tables, figures, and narratively as appropriate. SCOPING REVIEW PROTOCOL REGISTRATION: Open Science Framework https://osf.io/7bv5p/.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.079
GPT teacher head0.479
Teacher spread0.400 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations9
Published2020
Admission routes1
Has abstractyes

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