Enhancing the use of technology in the long-term care sector in Canada: Insights from citizen panels and a national stakeholder dialogue
Bibliographic record
Abstract
Enhancing the use of technology in long-term care has been identified as a key part of broader efforts to strengthen the sector in the wake of the COVID-19 pandemic. To inform such efforts, we convened a series of citizen panels, followed by a national stakeholder dialogue with system leaders focused on reimagining the long-term care sector using technology. Key actions prioritized through the deliberations convened included: developing an innovation roadmap/agenda (including national standards and guidelines); using co-design approaches for the strengthening the long-term care sector and for technological innovation; identifying and coordinating existing innovation projects to support scale and spread; enabling rapid-learning and improvement cycles to support the development, evaluation, and implementation of new technologies; and using funding models that enable the flexibility needed for such rapid-learning cycles.
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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 | 0.035 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.042 | 0.015 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".