Practicum-education experiences: post-interns' views
Bibliographic record
Abstract
The practicum component in undergraduate education across all professions (identified by various terms such as `internship,' `field education,' `clinical experience' or `co-op education') is typically rated by pre-baccalaureate students as the most important phase of their entire professional preparation. In this investigation, which formed one segment of a broader cross-Canada study, a group of postpracticum Engineering students from one Canadian university (who had just completed an internship with engineering firms) identified the most positive and the most negative aspects of that practicum experience. The authors compared these students' responses with those reported by post-practicum students from two other professions: Nursing and Teacher Education. Several positive aspects were identified by all three groups of students, such as: developing their professional competence and technical skills, increasing their personal self-confidence, and gaining real-world experience. Some of the negative aspects that all three cohorts mentioned were: receiving unsatisfactory internship placements, experiencing inadequate mentorship, and being assigned unproductive work tasks. The authors contend that practicum organizers across all professional fields should exchange with one another and examine such student data. The student voice provides a valuable dimension to the program-enhancement process, the ultimate goal of which, is to improve the `experiential learning' phase of professional pre-training in all fields.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".