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Record W4366463608 · doi:10.3148/cjdpr-2023-007

An Exploration of Dietetic Students’ Experiences in a Noncourse-based Service-Learning Opportunity in a Canadian Academic Setting

2023· article· en· W4366463608 on OpenAlexaffvenueabout
S. Hassib, Anahita Djalilvand, Danielle S. Battram

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsWestern University
Fundersnot available
KeywordsFocus groupModerationMedical educationService-learningPsychologyService (business)MedicinePedagogySociology

Abstract

fetched live from OpenAlex

Purpose: To investigate the experiences of dietetic students in a faculty-supervised, noncourse-based service-learning (NSL) opportunity called Nutrition Ignition! (NI!) to understand how NSL activities contribute to dietetic education. Methods: This study used focus group methodology. A convenience sample was recruited from current members of NI!. Participants completed a brief demographic questionnaire and then engaged in a focus group discussion led by a trained moderator who followed a semi-structured guide. Six focus group discussions were transcribed, and a common theme template was developed by researchers. Results: Out of 46 eligible members of NI!, 33 agreed to participate. The main reasons participants joined NI! were to develop professional skills and to help children in the community. Participants discussed many outcomes from their participation in NI!, including enhanced communication skills, especially in terms of knowledge translation; increased ability to be flexible and adapt to “real-world” situations; deeper awareness of the research process; and expanded world view. Conclusion: This study suggests that NSL is an effective way to build dietetic students’ personal and professional skills and provide an additional opportunity in academic settings to prepare dietetic students for entry-level 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)0.025
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation 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 score0.232
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.247
GPT teacher head0.489
Teacher spread0.242 · 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.

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

Citations1
Published2023
Admission routes3
Has abstractyes

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