Pedagogical Approaches Utilized in Nutrition Education: A Scoping Review
Bibliographic record
Abstract
Background: In nutrition and dietetics programs, active learning strategies are recommended to maximize student learning, but many instructors still default to a lecture-based teaching approach. Since instructors are trained as disciplinary experts, not pedagogical experts, an overview of pedagogical methods currently utilized in the discipline will allow instructors an introduction to this body of knowledge and provide Scholarship of Teaching and Learning (SoTL) scholars with a way to identify gaps for future work. Objective: To explore the current literature on pedagogical strategies utilized within nutrition and dietetics undergraduate programs in the USA and Canada, using the Joanna Briggs Institute guidance for systematic and scoping reviews. Methods: Six electronic databases were searched in the date range 2010-2020 for articles describing pedagogical methods used in teaching undergraduate nutrition/dietetics students. Data were extracted by two librarians using predefined terms, and another two team members independently screened the articles using Covidence, then narratively synthesized. Results: Thirty-eight articles met the inclusion criteria. A range of pedagogical methods were used: experiential learning/service learning (7), incorporating research (7), Interprofessional education (6), blogging (4), case-based learning (2), measuring critical thinking (2), Flipped classroom (2) and one article each from problem-based learning and other pedagogical methods. Conclusions: The most commonly used pedagogical methods in nutrition and dietetics education were experiential learning and research experiences. The popularity of these two research methods reflects higher education’s greater focus on out-of-classroom learning.
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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 | 0.035 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.039 | 0.036 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".