Impact of the COVID-19-induced shift to online dietetics training on PDEP competency acquisition and mental health
Bibliographic record
Abstract
Purpose: A pilot study to investigate the impact of the COVID-19 pandemic and shift to online learning and practicum training on dietetics students’ perceptions of Partnership for Dietetic Education and Practice (PDEP) competency acquisition and mental health. Methods: Dietetics students (n = 19) at the University of Guelph (2020–2021) were invited to complete an anonymous online survey to assess self-reported online dietetics practicum training experiences including (i) benefits and challenges, (ii) PDEP competency acquisition, and (iii) impact on mental health. Results: The benefits of online dietetics training included schedule flexibility (42.1%), reduced commute time (31.6%), and acquiring virtual counselling experience (21.1%). Reported challenges were insufficient communication with preceptors (36.8%), increased project workload (57.9%), and technology (15.8%). In online practicum placements, 52.6% of dietetics students reported adequately acquiring all PDEP competencies, with Nutrition Care identified as the most challenging to obtain (63.2%). A negative impact on mental health and increased levels of stress/anxiety were reported in 94.7% of trainees. Notably, 63.2% of students favoured continuation of online dietetics training through a hybrid or entirely online format. Conclusion: Online dietetics training has the potential to complement the traditional in-person model; however, further adaptation is required to optimize PDEP competency acquisition and students’ mental health.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".