Developing Future Leaders in Health Assessment Research: Evaluation of interRAI’s inSPIRe Program
Bibliographic record
Abstract
This article reports on the fourth interRAI Summer Program of International Research (inSPIRe), an intensive capacity-building initiative with a structured program, hosted at McMaster University in July 2024. Twenty-four delegates from 14 countries attended, representing diverse backgrounds in research, clinical practice, policy, and health informatics. The inSPIRe initiative aimed to foster understanding of interRAI's assessment systems, develop methodological skills, establish mentorship relationships, create opportunities for contribution to the interRAI consortium, and initiate global collaborations. All participants reported that the program met or exceeded their expectations, with significant benefits including access to comprehensive international datasets, engagement with experienced mentors, and effective knowledge translation between research and practice. Regional adaptations of the program have already emerged, demonstrating its scalability and impact beyond the initial intensive experience. The inSPIRe program represents an effective and flexible model for building global health services leadership and research capacity and capability, applicable internationally.
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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 | 0.202 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".