Integrated care for people with complex needs: evaluation of the V1SAGES Approach over the last decade
Bibliographic record
Abstract
1. Context: Eighteen percent of people in primary healthcare face interacting challenges among the physical, mental, and social dimensions of health, experiencing the most complex needs and resulting in decreased quality of life as well as increased disability and mortality risk. The organization of services for this population calls for integrated care. 2. Objective: This presentation aims to report on the development and evaluation of the V1SAGES Approach over the last decade, to improve integrated care for adults with complex needs, and to present the V1SAGES Approach toolkit supporting its implementation. 3. Study Designs: The approach was developed through many study designs within a participatory culture involving stakeholders across Canada: literature reviews (scoping, systematic, and realist); qualitative studies; case studies; a randomized controlled trial; logic model and analysis; and validation of measurement tools, including a case-finding tool. 4. Setting: Case managers in hospitals and care navigators in primary care clinics. 5. Population Studied: People with complex needs who frequently use health services. 6. Program: Case managers and navigators evaluate, plan, and coordinate services around the patient’s and their family’s needs in close collaboration with providers at all levels of healthcare and community organizations. The standard of care proposes to: identify adults with complex needs; assess their needs, life project, and preferences; develop an individualized services plan (ISP) with relevant partners during an in-person or virtual meeting; coordinate care among all providers according to the ISP; offer self-management support and education to the person and their family. The toolkit includes five web-based modules, the CONECT-6 case-finding tool, the standard of care, and all clinical tools. 7. Results: Our studies reported that the V1SAGES Approach was effective, reducing psychological distress and improving the patient’s sense of security. Facilitators and barriers to the implementation will be presented. 8. Conclusions: The evidencebased V1SAGES Approach and toolkit could be useful to researchers, clinicians, and decision-makers engaged in improving the health of people with complex needs, their experiences of integrated care, and their satisfaction with care services. This approach could also improve the healthcare system’s efficiency and reduce its economic burden.
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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 | 0.103 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".