Employment Outcomes among Registered Dietitians following Graduation in Manitoba
Bibliographic record
Abstract
Purpose: The study objectives were to (i) describe employment outcomes among Registered Dietitians (RDs) who graduated from the University of Manitoba, (ii) test for differences in employment outcomes according to graduation year, and (iii) compare preferred area of practice and geography prior to employment with past and current employment. Methods: Graduates of the Human Nutritional Sciences program (2006–2015) were invited to participate in an online survey. Data on respondent demographics, education, and employment outcomes were collected. Results: Overall, 133 (68%) respondents self-identified as RDs. RDs who had graduated between 2006 and 2011 were significantly more likely to secure employment within 6 months post-graduation compared with RDs that graduated between 2012 and 2015. Geographically, although 56% of RDs did not wish to gain experience in rural/remote communities upon graduating, 44% of these respondents reported working part- or full-time in a rural/remote location at some point during their career. Conclusion: Findings indicate that a substantial number of RDs in Manitoba are employed in a rural or remote location despite acknowledging that it is not a preferred location. Future research is needed to explore the views and experiences of new and established RDs toward rural or remote practice, including preparedness for practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".