“Forgetting the Pain”: Successful Applicants’ Experiences of Attaining a Dietetic Internship Position in Ontario
Bibliographic record
Abstract
This study seeks to explore the lived experience of students who applied to an Ontario-based dietetic internship program and were successful upon their first application attempt. A 32-item online survey was distributed via email to all students who graduated from Brescia University College, Guelph University, and Ryerson University between 2006 and 2011 and to members of the Dietitians of Canada Student Network, Toronto Home Economics Association, and Ontario Home Economists in Business. The final survey item invited respondents to participate in one-on-one interview. The semi-structured interviews focused on participants’ experience of applying to and receiving an internship position. Interviews were conducted either in person or by telephone and were audio recorded, transcribed verbatim, and thematically analyzed by the research team. Of the 82 participants who completed the online survey, 17 respondents participated in a one-on-one interview. Even students who were successful at attaining an internship are negatively impacted by having to compete with peers for an internship spot in Ontario. This research serves as a comparison to previous work examining the experiences of not attaining an internship. Both studies point directly to the changes urgently required to enhance the current model of education and training in Ontario.
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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 | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".