IMPLEMENTING A PROCESS OF RISK-STRATIFIED CARE COORDINATION FOR OLDER ADULTS IN PRIMARY CARE
Bibliographic record
Abstract
Primary health care may be the best place within the health system to coordinate care for older persons, but at present is poorly equipped to do so. Recent reviews found that an effective primary care model for complex patients requires appropriate targeting, engagement of patients and caregivers, and coordination with other services. This project aimed to understand the perceptions and experiences of providers, patients and caregivers with implementation of processes to achieve these aims. The Chronic Care Model and a multi-level framework for implementation of health innovations guided this study. Data collection and analysis followed a developmental evaluation approach. Data were collected using observations, individual interviews, a risk-stratification tool and tracking forms. Six patients, two family caregivers, and 13 providers were purposefully sampled from three primary care settings (rural and urban). Following implementation of a risk screening tool and an online referral mechanism, 560 patients were screened for level of risk, with care coordinated based on level of need. Although the screening and referral process took additional time in a busy practice context, health care providers, patients and caregivers identified many benefits. These included early identification of service need, greater awareness of community services available, and improved relationships between patients and providers. A process of risk-stratified care coordination offers potential benefits for older patients, caregivers and providers. However, taking the time to have meaningful conversations with patients was a challenge, and organizational structures and funding models may need to be modified to support fuller 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 | 0.039 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".