Development and dissemination of an ethical guidance and person‐centred isolation care planning tool to support the care of people with dementia during the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: Long-term care (LTC) residents have been disproportionately impacted by the COVID-19 pandemic, both from the virus itself and the restrictions in effect for infection prevention and control. Many barriers exist in LTC to prevent the effective isolation of suspect or confirmed COVID-19 cases. Furthermore, these measures have a severe impact on the well-being of LTC residents. Our aim was to develop a guide for long-term care to address the ethical challenges associated with isolating dementia patients during the pandemic. The Dementia Isolation Toolkit (DIT) was developed by members of the research team in partnership with LTC stakeholders to address: 1) the practical challenges of isolating or quarantining people with dementia in a compassionate, safe, and effective manner; and 2) the need for ethical guidance to support decision-making regarding isolation and infection control in LTC, to prevent indecision and moral distress. To develop the DIT the team reviewed and synthesized the literature on pandemic ethics in a plain-language document, which was then reviewed by our partners and stakeholders. The final ethical guidance tool includes a discussion of the ethics around infection control measures in a pandemic, an ethical decision-making tool, and a person-centred isolation care planning tool. The ethical guidance tool has been downloaded more than 6500 times since it was published (bit.ly/dementiatoolkit), and has been disseminated internationally. The worksheets are being used during outbreaks to support care and decision-making, as well as proactively, to prepare for outbreaks by developing isolation care plans. There is a need for support for ethical decision-making in the context of a pandemic, particularly in settings such as LTC. Future studies will evaluate the implementation of the tool and its impact in addressing moral distress in health care providers in long-term care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".