Changes to Canadian Dietetic Education Models 1993–2021
Bibliographic record
Abstract
Purpose: There have been many changes to both academic and practical aspects of dietetic education in Canada since 1993. This study sought to document and explore major changes. Methods: Key informants (n = 22) identified through purposeful sampling completed semi-structured interviews, using a draft timeline based on a literature review. Recordings were transcribed, coded, and analyzed thematically using a social ecologic framework. Results: Five main themes emerged: (i) challenges with the traditional dietetic education model; (ii) emergence of champions for change; (iii) barriers and facilitators for change; (iv) shift towards integration; and (v) increasing access for diverse populations. The interviews supported that changes had been driven by a need to increase access to dietitians and to improve the capacity and sustainability of dietetic education. Conclusions: The past thirty years have been marked by changes in the organization and delivery of dietetic education in Canada, mainly through collaborations of university programs with health system and community partners. Overall, dietetic education programs have increased their capacity and sustainability. By doing so, they have improved access and better positioned the profession to meet the needs of diverse populations. These findings provide context for dietitians, educators, and students to prepare for future development of the profession.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".