Oral Abstract Presentations at the 9th Canadian Conference on Dementia (CCD) Toronto, November 2017
Bibliographic record
Abstract
Background: Most individuals with dementia reside in community settings and this population requires a variety of supports and services to remain at home and maintain optimal quality of life.Capacity planning for dementia-related health services is important to understand the current needs of persons with dementia and plan for their future needs.Our project developed a Dementia Capacity Planning model for the province of Ontario in Canada to understand the current and future numbers of individuals with dementia in Ontario and anticipate their health service utilization.Methods: A validated diagnostic algorithm was used with provincial administrative health care databases to identify the historical number of community-dwelling individuals with dementia in Ontario between 2010-2015.The historical patterns of health service utilization including home care services, and applications for long-term care (LTC) admissions were then determined.Agent-based simulation models were then used to simulate future numbers of community dwelling individuals with dementia in Ontario as well their future health service utilization based on demographic trends and current health system resources. Results:The number of community-dwelling individuals with dementia increased from 81,637 individuals in 2010 to 106,427 individuals in 2015 (+30%).Using conservative estimates, the simulation model projected that this number will increase to 143,557 by 2020 (+35% over 2015).The total number of individuals with dementia in the community who have requested admission to LTC and were awaiting placement increased from 12,866 in 2010, to 22,063 in 2015 (+71%) and this number is projected to increase to 39,789 individuals by 2020 (+80% over 2015).The annual number of personal support worker hours provided by home care services is projected to be over 11 million hours by 2020, an increase of 50% over 2015 utilization. Conclusions:The growing population of older adults with dementia in Ontario will result in significant increased demands for home care and LTC services.Simulation models can be useful to understand these future demands and help in planning services accordingly.Significant changes to health services and supports for persons with dementia will likely be required to support this growing and vulnerable population in Ontario in the future. Dementia, Including Alzheimer's Disease, in Canada: Prevalence, Incidence and All-Cause Mortality Trends from the Canadian Chronic Disease Surveillance 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.393 | 0.072 |
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".