<i>Performance Issues of Dietetic Interns:</i>A Dietetic Educator's Perspective
Bibliographic record
Abstract
PURPOSE: Dietetic internships provide practical experience leading, in most cases, to the attainment of entry-level dietetic competence. Problematic intern performance issues were examined, as were how educators resolve these issues and the supports they require to manage them. METHODS: A survey was electronically distributed to all Dietitians of Canada internship/university course directors (n=57). The response rate was 40% (n=23). RESULTS: Annually, 61% of internships involve challenging performance issues related to intern knowledge, skills, attitude, and behaviour. These issues manifest themselves individually or in combination as an intern's inability to apply/demonstrate appropriate knowledge/skill, a view/approach to the profession that is not in keeping with the organizational view, an attitude that is in conflict with program values, a negative response to feedback, an inability to relate to others, work habits that are in conflict with program values, and personal attributes that detract from the ability to meet program expectations. Educators respond to these issues by modifying their communications, the learning environment, and the program. CONCLUSIONS: Educators' strategies could be enhanced through consultation with other educators, mentor training, and the development of formal procedures.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".