Pandemic Preparedness and Beyond: Person- Centred Care for Older Adults Living in Long- Term Care during the COVID-19 Pandemic
Bibliographic record
Abstract
The increasing complexity of residents' needs, emphasis on social distancing and limited access to high-quality support presented challenges to patient-centred care during the pandemic.Yet the pandemic created an opportunity to explore novel approaches to achieving person-centred care within long-term care (LTC).We share three projects designed to enhance care delivery in the context of the pandemic: to address personhood needs during outbreaks, to improve the quality of medical care and to deliver personalized palliative and end-of-life care using a prediction algorithm.These projects enabled better care during the pandemic and will continue to advance person-centred care beyond the pandemic. Key Takeaways• Transformative changes and innovative integrative care models that aim to build capacity within long-term care are required to address the ongoing and complex care needs of residents who receive care in this setting.• The pandemic has offered an opportunity to create innovative approaches to how person-centred care can be provided in an under-resourced healthcare setting.The partnership with research teams has accelerated the development of context-and environment-specific tools and resources for LTC.• Solutions designed to support person-centred care must be flexible and adaptable to the environment.Allowing LTC providers to articulate the needs and goals of their own homes has been essential for motivating change.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".