Bibliographic record
Abstract
Opportunities and Challenges in Implementation The value of the interRAI hospital systems with their case-finding tools and “targeted” assessment systems at each stage of care supported by a core nurse- administered assessment suitable for all adult patients is now becoming recognized by hospital administrators and clinicians around the world. It will enable better identification, diagnosis and treatment and even risk-avoidance of geriatric syndromes as well as the reduction in the efficiency and redundancy of current clinical care systems and processes is where the greater opportunity of this suite exists. Implementing new systems that are not aligned with deeply engrained thinking and ways of working creates potential serious challenges. This presentation will assist clinicians and administrators to understand and appreciate the opportunities that the implementation of the interRAI suite could provide their environments and how to recognize and effectively address common challenges that may arise in advancing its implementation.
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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 | 0.208 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.022 | 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".