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Record W4225293748 · doi:10.3148/cjdpr-2022-006

Dietetic Students’ Perceptions of Learning Professional Competencies with Four Simulations Throughout a Semester

2022· article· en· W4225293748 on OpenAlexaffvenue
Mylène Rosa, Isabelle Giroux

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsInstitut du Savoir MontfortUniversity of Ottawa
Fundersnot available
KeywordsMedical educationPerceptionPsychologyMedicine

Abstract

fetched live from OpenAlex

In nursing and medicine, taking part in simulation activities has been shown to be beneficial for students' learning; however, little has been documented in dietetics. This study aimed to document the perceived effect of 4 simulations on development of professional competencies by dietetic students. A mixed-method convergent approach was used with pre- and post-questionnaires, interviews, and a focus group discussion to look at dietetic students' perceptions of learning as part of a Nutrition Assessment course. Nonparametric tests for questionnaires and theme analysis for transcripts were used to examine data. After analysis, data were compared and merged for interpretation. Results showed that participants perceived a significant increase in comprehension of various competencies with simulations. In interviews and a focus group, a participant subgroup (n = 7) perceived an enriched understanding of some dietetic competencies compared with pre-simulations. Simulations seemed to have transformed classroom concepts to a more practical understanding of dietetic practice. More studies are needed to identify if these results could be replicated in different settings. Simulations had a positive effect on students' perception of competencies development and may be an andragogical tool of choice to support preparing future dietitians for entry to practice.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.162
GPT teacher head0.506
Teacher spread0.344 · 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 designObservational
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

Citations2
Published2022
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Dietetic Practice and ResearchSame topicDietetics, Nutrition, and EducationFrench-language works237,207