Transforming Care for Older Adults Living with Complex Health Conditions in Ontario Post-Covid: Conference Proceedings and Recommendations
Bibliographic record
Abstract
The virtual conference 'Transforming Care: Supporting Older Adults Post-COVID in Ontario' was held in October 2021. It was organized by Specialized Geriatric Services (SGS) East and held over three half-days. The guiding themes included: The Need, The Innovation, and The Transformation. Over 500 participants heard from ~50 clinicians, researchers, administrators, older adults, care partners, and community partners. The pandemic uncovered and exacerbated existing issues and pushed us to explore new ways to support older adults living with complex health conditions. The following key priorities were identified: older adults and their care partners call for personalized care experiences, and a lifespan approach to care delivery; aging in the community remains the most common preference; an integrated community care system that supports aging at-home should be prioritized; care delivery by SGS interprofessional teams and specialists is paramount to providing comprehensive care; building health human resource capacity should be a system priority; and promising innovations should be scaled and spread. Evidence shows that we cannot return to status-quo; post-pandemic planning of both who we serve and how we serve needs to be anchored in system renewal, not just recovery. Renewal means integrating lessons learned during the pandemic into the redesign of our systems of care. Investments in innovative, upstream strategies that support home and community-based care, and target health promotion and prevention are necessary. The provincial and regional infrastructure of SGS has the expertise and capacity to assist Ontario Health Teams in responding to the evolving health and social needs of this population.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".