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Record W4414472566 · doi:10.3148/cjdpr-2025-023

Assessment of Scientific Literacy Skill Development Essential for Evidence-Based Practice During Combined Dietetic Practicum Training and Graduate-Level Coursework

2025· article· en· W4414472566 on OpenAlexaffvenue
Kelsey Van, Rachel K. von Holt, David M. Beauchamp, Teresa Siby, Katie Langan, Melissa Verch, Alexia Prescod, Justine Keathley, Jennifer M. Monk

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPracticumCourseworkFluencyLiteracyTraining (meteorology)Test (biology)

Abstract

fetched live from OpenAlex

Purpose: An exploratory longitudinal cohort study to assess scientific literacy (SL) skill development (both practical and self-perceived capabilities) in the Master of Applied Nutrition program. Methods: Students (n = 22) completed an optional online survey at the start and end of the combined coursework and practicum training portion of the program. Practical SL skills were assessed using the validated Test of Scientific Literacy Skills. Results: Both practical and perceived SL capabilities increased during the program with the largest increases in the ability to draw conclusions based on quantitative data and to read and interpret graphical data. Critical assessment of scientific literature validity was an identified training gap. Conclusion: SL skills are necessary for evidence-based dietetic practice and are critical skills that are acquired in the program; however, targeted approaches to minimize training gaps are required.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.378
GPT teacher head0.583
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
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

Citations0
Published2025
Admission routes2
Has abstractyes

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