Sentinels of the Social geriatric program: implementation in rural areas of network for the identification of seniors at rick secondary to loss of autonomy or cognitve deficits
Bibliographic record
Abstract
Abstract Background In a large rural area, isolated seniors rarely consult health services and often come to emergency rooms after severe deterioration. The implantation of “sentinels” in their environment could make it possible to identify them early. Method The AGE Foundation, a charitable organization, in collaboration with the public health services of the Granit region (22,000 p/2731 Km2) in the province of Quebec developed care trajectories, identified caregaps and environments requiring sentinels. The project include the analysis of the implantation and the identified cases Result In 2022, the nurse “navigator” trains 80 sentinels in the use of tracking tools such as AD‐Plus. The sentinels are home care workers and non‐professionals (hairdresser, grocer, municipal agents, etc.) well established in 16 communities. In 9 months they have raised more and more alerts, from 2 to 25 per month for a total of 129. The alerts concerned seniors with an average age of 79, 56 were between 80 and 89 years old and 18 over 90 years old. The majority, 66%, are women. But more importantly, 73% had no follow‐up in the health system before. They required an average of 20 days of targeted interventions, the vast majority of which took place at home. Conclusion Many seniors suffer from loneliness. At the beginning of loss of independence or cognitive impairment, do not consult health services. The “Sentinels” and the Navigator present an effective tracking model.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".