EXPERIENCES AND PERSPECTIVES OF CANADIAN UNIVERSITY EDUCATORS IN INCORPORATING FOOD LITERACY INTO UNDERGRADUATE FOOD AND NUTRITION PROGRAMS
Bibliographic record
Abstract
Background: Food literacy is an emergent concept in both research literature and health promotion efforts. It is described as a set of practical tools and critical perspectives, situated within a socio-ecological context, that can help individuals to develop a positive relationship with food and eating (Cullen et al., 2015; Colatruglio & Slater, 2014). Food literacy has the potential to help us understand and remedy our dietary patterns, that in the last 50 years, have led us to high rates of dietary related chronic diseases as well as broader humanitarian problems such as issues surrounding social justice and planetary health. In 2020, food literacy was added as a required competency to Canadian educational standards for dietetic education (i.e., Integrated Competencies for Dietetic Education and Practice (ICDEP) Version 3). To date, there are no known published studies that have looked at the incorporation of this topic into undergraduate food and nutrition programs. Understanding how best to train nutrition students on this topic can lead to insights that may facilitate future learning and ultimately impact population level changes in eating behaviours. Objective: To explore the experiences and perspectives of dietetic educators in incorporating food literacy into undergraduate food and nutrition programs across Canada. Methods: Semi-structured interviews were conducted with dietetic educators from across Canada from November 2021 to April 2022, who teach a variety of courses within accredited food and nutrition academic programs. Interviews were conducted by Zoom and were audio recorded and transcribed. NVIVO version 12 (QSR International, Doncaster, Australia) software was used to help manage the data. Interpretive Description was the qualitative approach used; this methodology aims for practical outcomes that can inform clinical practice (Thorne, 2008). Data analysis involved interpreting the data which resulted in creating codes and then categories with an eventual development of themes. Results: Eighteen participants completed the interviews and average interview length was approximately one hour (range: 28 minutes to 1 hour and 54 minutes). Following the analysis of the data, four themes were developed that further our understanding of integrating food literacy into food and nutrition programs. The first theme was “Food literacy is important to the Dietetic Profession.” This theme was supported by four sub themes: addition of food literacy to dietetic education is enthusiastically embraced; philosophical alignment with food literacy; food literacy is important to dietetic practice; and changing attitudes in the dietetic profession towards embracing concepts like food literacy. The second theme was “Conceptualization of Food Literacy is Still Evolving.” The sub themes for this included: emphasis on food skills & food knowledge; a positive view of a food literacy definition; food literacy is too aspirational; and practical use of food literacy is questioned. The third theme was “Aspects of Food Literacy Well Represented in Curriculum, But There is Room for Growth.” This theme was supported by the following sub-themes: food literacy taught throughout many courses; few to no changes made to curriculum; and teaching of some ecologic components could be enhanced. The fourth and final theme was “Food Literacy’s Incorporation in Curriculums is Challenged by Unclear Key Concepts.” The sub-themes that provide the foundation for this theme were: lack of an operational definition for food literacy; lack of clarity in assessing the food literacy practice competency; and students’ understanding of the role of dietitians. Conclusion: The enthusiastic reception for food literacy in the educational standards reflects these dietetic educators’ appreciation for situating nutritional health within a socio-ecological context. Because food literacy is a complex topic that is still emergent in research as well as new to dietetic education, more supports and resources will be helpful to aid in incorporating food literacy successfully into undergraduate food and nutrition programs. In particular, an operational definition of food literacy which considers this concept in the context of dietetic education and practice will be beneficial. As well, extra supports and instruction is recommended for educators to be able to use the ICDEP assessment tool to evaluate student achievement of the food literacy practice competency.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".