Transcending Distance in Long-Term Care: Can Serious Games Increase Resident Resilience?
Bibliographic record
Abstract
In Canada, over 15,000 residents of long-term care have died from COVID-19 since the start of the pandemic representing 59 percent of all COVID-19 deaths (National Institute of Ageing, 2021). Urgent research and subsequent applied action are needed to save life and quality of life including the presence of family (CFHI, 2020). Social and physical frailty are major systemic patient safety gaps and are challenges for most healthcare organizations. This practitioner-led panel of experienced human factors, implementation science and healthcare experts used a case study of a project at North York General Hospital’s Seniors’ Health Centre in Toronto to discuss how these challenges can be addressed with serious games. The project discussed used games that aim to reduce social and physical frailty through exercise while interacting with remote families. Lessons learned to-date and challenges observed, in rapidly implementing safety and human factors programs intended to create resilient residents in a real healthcare context were discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".