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Record W1664141142 · doi:10.3148/cjdpr-2015-018

Examining the Cultural Competence of Third- and Fourth-Year Nutrition Students: A Pilot Study

2015· article· en· W1664141142 on OpenAlexafffundvenueabout
Rebekah Hack, Sharareh Hekmat, Latifeh Ahmadi

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsWestern University
FundersBrescia University College
KeywordsCompetence (human resources)PsychologyCultural competenceMedical educationMedicinePedagogySocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to provide preliminary Canadian research assessing nutrition students' cultural competence and to identify areas for future education initiatives in dietetic education that could ultimately improve dietitians' cultural competence. A mixed-methods study was conducted using a 24-item questionnaire that was administered to students enrolled in third- and fourth-year undergraduate nutrition classes (n = 133). In total, 115 questionnaires were analyzed for quantitative data, and 109 were analyzed for qualitative data. The students scored an overall medium-high level of cultural competence. Out of the 5 areas examined (skills, attitudes, awareness, desires, knowledge), students' multicultural knowledge scores were the lowest. It was found that a lower number of barriers to learning about other cultures were significantly associated with a higher overall cultural competence score, and taking a course in cultural foods significantly increased the students' knowledge and overall cultural competence (P ≤ 0.05). The qualitative data found that students felt the cultural competence curriculum had gaps and identified several ideas for improvement. In conclusion, this research data provides novel insights into the cultural competence of Canadian dietetic students and additionally supports future research and curriculum development to enhance cultural competence.

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)0.009
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.341
GPT teacher head0.487
Teacher spread0.146 · 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

Citations17
Published2015
Admission routes4
Has abstractyes

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