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Record W3181681668 · doi:10.32920/cd.v6i1.1452

Systemic and institutionalized racism, not achievement gap factors, limit the success of Black, Indigenous, and People of Color in dietetics education and credentialing

2021· article· en· W3181681668 on OpenAlexvenueno aff
Kate Gardner Burt, Rosemary Lopez, Mario Landaverde, Ashtar Paniagua, Erika Avalos

Bibliographic record

VenueJournal of Critical Dietetics · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsRacismDisadvantageIndigenousPsychologyCredentialingTechnicianGerontologyEthnic groupMedicineMedical educationSociologyPolitical scienceGender studies

Abstract

fetched live from OpenAlex

Our aim was to explore racial/ethnic differences on achievement and opportunity gap factors in nutrition students and identify factors related to the pathway to become a Registered Dietitian Nutritionist (RDN). An online survey was completed by 1447 current or recent dietetic students and interns, some of whom identified as RDNs and/or Nutrition and Dietetic Technician, Registered (NDTRs). The survey consisted of validated scales measuring academic confidence, mentoring, racial climate, grit, and time management, and questions measuring socio-economic factors. Analysis included descriptive statistics, multiple regression, t-tests, and chi-squares. No differences were observed between the scores of Black, Indigenous, and participants of color (BIPOC) and White participants on academic scales. BIPOC experienced a more negative racial climate than White participants (p<0.05). Black dietetics students are also at particular economic disadvantage compared to other participants of color. Ultimately, Black and BIPOC are as academically prepared as White participants but institutionalized and structural racism (e.g., opportunity gap factors) limit their opportunities to succeed.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.064
GPT teacher head0.409
Teacher spread0.346 · 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 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

Citations8
Published2021
Admission routes1
Has abstractyes

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