<i>Learning Opportunity and Preparedness for Practice:</i> Perceptions from Dietetics Programs in Canada
Bibliographic record
Abstract
PURPOSE: This study determined dietetics trainees' and program coordinators' perceptions about trainees' preparedness to practice, based on Dietitians of Canada's 145 competency statements. Depth and breadth of learning opportunity were also determined with definitions of these two concepts based on Elliott's view of professional education and practice. METHODS: Research questions were: 1. How prepared were trainees for practice? 2. What were the depth and breadth of learning opportunity in assessment, planning, implementation and evaluation? 3. How many learning opportunities were there in professional practice and communication? 4. Did responses vary between integrated programs and internships or between trainees and program coordinators? RESULTS: Of 313 trainees, 168 (54%) responded and 23 (72%) of 32 coordinators responded. Preparedness was rated as "well prepared" or better for 25 (56%) of the 45 main competencies. For every competency, preparedness ratings were higher in integrated programs than in internships. Learning opportunities were rated as sufficient in depth and breadth or number for 88 (61%) of the 145 competency statements. Low ratings for preparedness were accompanied by low ratings for depth and/or breadth or number of learning opportunities. CONCLUSIONS: The notion of depth and breadth is useful as a framework to assess learning opportunities for developing entry-level competence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".