Barriers and facilitators to person-centred infection prevention and control: results of a survey about the Dementia Isolation Toolkit
Bibliographic record
Abstract
Abstract Background People working in long-term care homes (LTCH) face difficult decisions balancing the risk of infection spread with the hardship imposed on residents by infection control and prevention (ICP) measures. The Dementia Isolation Toolkit (DIT) was developed to address the gap in ethical guidance on how to safely and effectively isolate people living with dementia while supporting their personhood. In this observational study, we report the results of a survey of LTCH staff on barriers and facilitators regarding isolating residents, and the impact of the DIT on staff moral distress. Methods We completed an online cross-sectional survey. Participants (n = 207) were staff working on-site in LTCH in Ontario, Canada since March 1, 2020, with direct or indirect experience with the isolation of residents. LTCH staff were recruited through provincial LTCH organizations, social media, and the DIT website. Survey results were summarized, and three groups compared, those: (1) unfamiliar with, (2) familiar with, and (3) users of the DIT. Results 61% of respondents identified distress of LTCH staff about the harmful effects of isolation on residents as a major barrier to effective isolation. Facilitators for isolation included delivery of 1:1 activity in the resident’s room (81%) and designating essential caregivers to provide support (67%). Almost all respondents (84%) reported an increase in moral distress. DIT users were less likely to report an impact of moral distress on job satisfaction (odds ratio (OR) 0.41, 95% CI 0.19-0.87) with 48% of users reporting the DIT was helpful in reducing their level of moral distress. Conclusions Isolation as an ICP measure in LTCH environments creates moral distress among staff which is a barrier to its effectiveness. ICP guidance to LTCH would be strengthened by the inclusion of a dementia-specific ethical framework that addresses how to minimize the harms of isolation on both residents and staff.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".