Exploring the implementation of <scp>COVID</scp>‐19 infection control guidance in congregate living settings supporting those with intellectual and developmental disabilities
Bibliographic record
Abstract
Abstract The COVID‐19 pandemic has put the lives of people with intellectual and developmental disabilities (IDDs) at risk, including those residing in congregate living settings. This study aimed to explore the experiences of congregate living agencies supporting individuals with IDD when implementing infection control guidance during the COVID‐19 pandemic for the purpose of identifying recommendations for future implementation. Interpretive description was the methodological approach used for this qualitative study. Data were collected through a semi‐structured focus group with administrative personnel from developmental services (DS) congregate living agencies supporting adults with IDD in Ontario, Canada. Data were analyzed using thematic analysis. Our findings identified successes and challenges related to the implementation of infection control guidelines in practice, as well as strategies used during the implementation of guidelines. Five main themes were identified—Communication, Collaboration, Finding and Managing Resources, Agency Capacity, and Future Considerations. Effective communication and collaboration within agencies, as well as between agencies and local public health units or governing ministries, led to the successful implementation of infection control guidance. Prior experience with pandemics, as well as managers with knowledge of infectious disease and infection control, was crucial in interpreting and implementing COVID‐19 infection control guidance. DS agencies experienced successes and challenges when implementing infection control guidelines. The needs of DS agencies and individuals with IDD should be prioritized when developing infection control guidance to ensure that implementation is feasible and appropriate for congregate living settings and the population supported.
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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 | 0.004 | 0.261 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".