Time for Change: Insight from the Learning Sciences to Inform Dietitian Education and Credentialing
Bibliographic record
Abstract
The current practice and policies utilized to credential registered dietitians nutritionists, referred to in this article as dietitians, for entry-into practice must be overhauled utilizing the evidence grounded in the learning sciences. The interdisciplinary field of the learning sciences provide evidence for effective educational practice and alternative assessment methods to reform dietetic education (Sawyer, 2006, p. xi). As a teacher in a dietetic education program and a graduate of a doctoral program in Curriculum, Instruction and the Science of Learning I will share my recommendations for dietetic education and credentialing of practitioners. In this article I will argue that the history of education in the United States has influenced the current education and credentialing process for dietitians. I will discuss the implementation of competency based dietetic education and the impact of the dietitian credentialing examination on the field. Finally, I will offer evidence to support my recommendations to implement alternative methods for credentialing dietetic practitioners that are supported in the learning sciences research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".