Case-finding for disabilities with PRISMA-7 in emergency rooms: evolution in Sherbrooke, Québec
Bibliographic record
Abstract
INTRODUCTION: The PRISMA-7 tool [1] has been introduced in two emergency rooms (ERs) in Sherbrooke to identify older people with significant disabilities. The seven yes/no questions had been included in the triage instrument for people aged 75 years and over. The positive cases were directed to the single entry point of the local health and social services centre, which then conducted assessment and eventually provided home care. The study's objective was to monitor the rate of PRISMA-7 use in ERs since its implementation (4 years ago). RESULTS: During the first year of implementation, the rate of PRISMA-7 use gradually increased up to 50–60%, then remained stable during the second and third years. This plateau can be accounted for, in part, by the scarcity of resources for assessing and delivering home-care services. The rate of PRISMA-7 use fell to 40% during the fourth year, which coincided with renovation of an ER. A 50% objective is in place. DISCUSSION: The rate of case-finding appears logical with the services actually available for assessing functional autonomy and the corresponding home services required. In terms of the population-health approach for supporting functional autonomy, it highlights the challenges in reaching the population level. As suggested by Young and Turnock [2], some managers consider publishing community-care waiting lists to increase attention and, consequently, priority in the health system.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".