Leveraging Promising Policies to Support Long-Term Care Residents' Quality of Life Post-Pandemic
Bibliographic record
Abstract
Abstract Long-term care (LTC) is highly regulated and often the policy language is complex and in tension with residents’ quality of life goals. Prior to COVID-19, LTC policy levers prioritized safety over other quality domains such as privacy, dignity, spirituality, and comfort. During the pandemic, this focus on safety regulations, while important, intensified in ways that often negatively impacted residents’ overall quality of life. In this symposium, we share findings from a five- year research project where we conducted a unique and expansive review of regulatory policy across four Canadian jurisdictions. We highlight how 11 different quality of life domains are supported and which texts offering promising policy language to enhance a well-rounded quality of life for residents. These are timely insights to offer as policy-makers look to the future and consider the lessons learned from the pandemic. We contend that creating more LTC policy is not a timely pathway forward to LTC reform. Instead, we suggest that existing policy can be leveraged when applied within a resident-centred quality of life lens. We will guide attendees through examples of existing promising policies highlighting how they might leveraged in planning for a better LTC system. The discussion will be rooted in our unique resident-centred approach to policy analysis using specific domains of quality of life and then applied to four different perspectives: residents, families, staff and volunteers. Our discussant a Ministry of Health decision-maker will address the implications of our research for post-pandemic planning to improve resident quality of life
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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 | 0.053 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| 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".