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Record W2604105794 · doi:10.3148/cjdpr-2017-004

The Nature of Competition in Dietetics Education: A Narrative Review

2017· review· en· W2604105794 on OpenAlexaffvenue
Jillian Ruhl, Daphne Lordly

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2017
Typereview
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsCompetition (biology)NarrativeCollegialityPsychologyMedical educationPedagogyOpposition (politics)Narrative reviewMedicinePolitical science

Abstract

fetched live from OpenAlex

The impact on student development associated with being part of a competitive program is an emerging finding in dietetics research. As such, a narrative review of relevant literature was conducted pertaining to competition in post-secondary programs and the educational experience of students with respect to developing a career in dietetics. The review was guided by 2 questions: How is competition experienced by students? What strategies can reduce competition among dietetics students within the educational experience? A refined literature screening process justified including 32 articles in the review. The overall review findings suggested that, across dietetics programs, the outcomes of competition negatively impacted students' personal identity, motivation in the learning process, and involvement behaviours, including collaboration among students, participation in academic opportunities, and student relationships with faculty. The implementation of strategies conducive to addressing the effects of excessive competition and building a supportive academic environment, for example increasing collegiality and engaging students in intrinsic learning, are important for continued growth of the dietetics profession. Educators can reflect on the complex nature of competition and how the effects of excessive competition may be in opposition with the competencies that students are expected to develop as professionals.

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)0.011
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.693
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
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 head0.305
GPT teacher head0.611
Teacher spread0.305 · 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 designNot applicable
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

Citations12
Published2017
Admission routes2
Has abstractyes

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