Reflections on Perceived Preparedness of Dietetic Internship Graduates Following Entry into Practice
Bibliographic record
Abstract
PURPOSE: To report on the perceived level of preparedness of dietetic internship (DI) graduates for entrance into practice as dietitians. METHODS: Graduates of an Ontario based, nonintegrated DI program from 2007-2011 who were at least 1 year postgraduation were surveyed to determine their level of perceived preparedness for practice using an electronic, content validated, self-administered questionnaire. RESULTS: Of 38 eligible graduates, 23 (61%) responded. Seventy-five percent of respondents were working as clinical dietitians, and 30% were working as community dietitians. Eighty-five percent of graduates reported feeling well or very well prepared for practice. Clinical and professional practice tasks were scored highest in terms of preparedness (ratings above 4.5/5) and research-related tasks such as using the research literature (4.1/5), making evidence-based decisions (4.2/5), and engaging in practice-based research (4.1/5) scored lower. Training gaps identified by 32% of respondents included community nutrition and management skill training. CONCLUSIONS: Overall, results indicate that this DI program provides a positive training experience that prepares its graduates for entrance into practice as dietitians. Qualitative comments identifying gaps and improvements have guided changes to the curriculum including strengthening community-based placements. Post-graduate surveys represent an important tool in assuring that training programs evolve to meet the needs of students entering the workforce.
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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".