COVID-19 Pandemic Effects on Job Search and Employment of Graduates (2015–2020) of Canadian Dietetic Programmes
Bibliographic record
Abstract
Purpose: Self-reported coronavirus 2019 (COVID-19) pandemic effects on dietetic job search, employment, and practice of recent graduates were explored within a national workforce survey. Methods: Graduates (2015–2020) who were registered/licensed dietitians or eligible to write the Canadian Dietetic Registration Exam were recruited through dietetic programmes, Dietitians of Canada’s communication channels, and social media. The online survey, available in English and French from August through October 2020, included questions about pandemic experiences. Descriptive statistics and thematic analysis were applied to closed and open-ended responses, respectively. Results: Thirty-four percent of survey respondents (n = 524) indicated pandemic effects on job search and described delayed entry into dietetics, fewer job opportunities, and challenges including restricted work between sites. The pandemic affected employment for 44% of respondents; of these, 45% indicated working from home, 45% provided virtual counselling, 7% were redeployed within dietetics, 14% provided nondietetic COVID-19 support, and 6% were furloughed or laid off. Changed work hours, predominantly reduced, were identified by 29%. Changes in pay, identified by 12%, included loss (e.g., raises deferred) or gain (e.g., pandemic pay). Fear of infection and stress about careers and finances were expressed. Conclusion: The COVID-19 pandemic profoundly affected both acquiring positions and employment in 2020 for recent dietetic graduates.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".