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Record W2895623558 · doi:10.3148/cjdpr-2018-027

Exploring Social Justice Advocacy in Dietetic Education: A Content Analysis

2018· article· en· W2895623558 on OpenAlexaffvenueabout
Kathryn Fraser, Jennifer Brady

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsAccreditationTerminologyContent analysisSocial justiceMedical educationPrioritizationEconomic JusticePsychologyPedagogyPublic relationsPolitical scienceMedicineSociologySocial scienceCriminology

Abstract

fetched live from OpenAlex

PURPOSE: To explore the extent to which knowledge- and skill-based learning regarding social justice and/or social justice advocacy is included in the course descriptions of required courses of accredited, English-speaking dietitian training programs in Canada. METHODS: This study is a mixed-methods content analysis of required course descriptions sampled from university academic calendars for accredited, English-speaking dietitian training programs across Canada. RESULTS: Quantitative analysis showed that required course descriptions (n = 403) included few instances of social justice-related terminology (n = 63). Two themes emerged from the qualitative analysis: competing conceptualizations of social issues and dietitians' roles; prioritization of science-based knowledge and ways of knowing. CONCLUSIONS: Accredited, English-speaking dietitian training programs in Canada appear to include little knowledge- or skill-based learning regarding social justice issues and advocacy. Supporting future dietitians to pursue leadership roles in redressing social injustices and socially just dietetic practice may require more explicit education and training about social justice issues and advocacy skills.

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)0.005
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.000
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 head0.567
GPT teacher head0.549
Teacher spread0.018 · 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

Citations15
Published2018
Admission routes3
Has abstractyes

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