Bibliographic record
Abstract
BackgroundWithin the context of Canada’s aging population, the complexities of its long-term care system, and the COVID-19 pandemic, information about long-term care facilities, their residents, and their workers is needed. However, little information exists on long-term care residents as they are rarely included in population surveys, and data on the facility characteristics of long-term care workers’ places of employment are often not available. Objective and ApproachTo effectively monitor changes, improvements, and health outcomes in Canada’s long-term care sector, many data gaps need to be addressed. This paper will present the results of an initiative that sought to examine the potential of integrating existing survey and administrative data sources to fill these gaps. A novel approach was taken which focused on how both facility-level and individual-level data could be integrated, allowing for a more comprehensive understanding of long-term care in Canada. ResultsThis initiative resulted in the development of two data linkage proposals. This presentation will provide an overview of the data sources identified for linkage, the approach taken to examine the feasibility of these linkages, challenges with integrating the data sets, and next steps to move the initiative forward. ImplicationsThe data linkages that will be discussed will help to address data gaps on Canada’s long-term care system. The proposed data linkages may also provide researchers with ideas for using similar data sources to address data gaps in long-term care in their respective countries.
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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 | 0.149 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.048 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| 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".