Unsuccessful Dietetic Internship Applicants: A Descriptive Survey
Bibliographic record
Abstract
PURPOSE: We examined the demographic characteristics of applicants who applied and were unsuccessful in securing an internship position, what these applicants did afterward in their efforts to obtain an internship position, and which career paths they pursued. We also searched for any differences in eligibility between applicants who had not obtained an internship position and those who eventually were successful. METHODS: A 68-item online survey was administered. RESULTS: The study sample (n=84) was relatively homogeneous: female (99%), heterosexual (98%), Caucasian (70%), Canadian-born (75%), having English as a first language (73%), multilingual (40%), and having completed a previous degree (29%). Mean self-reported cumulative grade point average (3.35) exceeded the minimum (3.0) required by most Ontario internship programs. Over 25% eventually secured an internship position. Applicants who rated their packages strong in community nutrition were less successful in attaining an internship. Little difference in qualification was found between those who were eventually successful and not-yet-successful applicants. CONCLUSIONS: Unsuccessful applicants met academic and other requirements for admission to dietetic internship programs in Ontario. Insufficient training opportunities, costs associated with internship, and competition may be contributing to a loss of human potential in dietetics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".