Inspiring That EUREKA Moment: The Importance of Patient Co-Design of Outputs from interRAI Assessments
Bibliographic record
Abstract
There are rich data available from assessments like the interRAI Mental Health that may not be shared with patients in usable ways. We outline a co-design process as an example for health system providers interested in developing platforms for sharing interRAI information with care recipients. Persons with lived experience (current and past service users) identified design specifications for summaries of interRAI data over a series of co-design workshops, including visual and text representations of clinical information as well as processes for sharing summaries with patients. Graphic and text considerations were identified in workshops, including icons associated with key clinical issues and nuanced text summaries of assessment outputs and implications. Participants noted that persons in inpatient care should review this information once stable, and with assistance of a peer support specialist. This work affirms that it is not only desirable, but entirely achievable from an organizational perspective to prioritize and integrate patient voices in the design and use of clinical information.
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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 | 0.152 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".