An International Interprofessional Health Quality Graduate Internship: The Shared Gains of Educational-Research Partnerships
Bibliographic record
Abstract
The challenges inherent in improving health quality and safety, as well as the intentions to address them, are seen globally. Like the healthcare teams themselves, multiple perspectives from varying professional backgrounds and contexts can best leverage ideas and solutions. As we provide advanced education about safety, quality and risk in health to current and future leaders, we have come to appreciate the importance of global connectedness and the exceptional opportunities that can be provided to students. This paper is about an innovative PhD in Health Quality program, which includes a practical internship. Specifically, we describe an international internship that occurred for one Canadian doctoral student with a UK research partner. This experience has confirmed the value of internships as applied to health quality and safety, and particularly as an opportunity to work with and learn from researchers with differing professional backgrounds. Further, this practical experience led to an expanded depth and breadth of knowledge (both theoretical and applied) for this doctoral student.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.027 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".