17 An arts-based approach to system improvement: provider experiences of COVID-19 in a group home
Bibliographic record
Abstract
Background The devastating effect of COVID-19 has been felt worldwide. Hospitals garnered significant attention, but the immense vulnerabilities in community settings-including nursing homes and assisted-living facilities-were quickly realized. As the crisis subsides, it is critical we learn from professionals who worked during this time, and generate evidence for informing resilience, particularly in long-term care. In respect of the traumatic, individualized, and sensitive nature of these experiences, and in appreciation of the need for depth of information from which to advance improvement, a qualitative approach is essential. Objectives 1) Describe the experiences of providers caring for residents with disabilities during a COVID-19 outbreak; 2) critically examine the enablers/challenges regarding their emotional well-being during the outbreak; and 3) develop context-specific strategies for change, and determining disaster preparedness. Methods Providers from a group home for adults with developmental and physical disabilities were recruited for this arts-based case study; in spring 2020 they had a COVID-19 outbreak and six residents died. Participants are making art (e.g., painting, poems) to express their experience, which is further explored in interviews/focus groups. Results Eleven providers have volunteered. They describe the outbreak as life-altering. A repeated descriptor is ‘war zone’; participants were not sure they would survive it. Unwillingness to leave residents-viewed as family-made for feelings of guilt when providers finally relented to going home despite having worked a 16-hour shift. Additional results will be shared. Conclusions One participant stated, ‘I think what we went through will be informative to others and I would hope to experience some healing through it as well.’ Meaningful, sustained improvement will not occur unless we systematically, respectfully document the words and unpack the complexities of what it was like for those who provided care during the crisis of COVID-19; only then can we build anew.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".