COVID-19 and social care screening for older adults in social housing: a CP@clinic adaptation
Bibliographic record
Abstract
BACKGROUND: COVID-19 has changed healthcare access and delivery, especially impacting older adults. The Community Paramedicine at Clinic (CP@clinic) program is a chronic disease prevention, management, and health promotion program for community-dwelling low-income older adults. We investigated a telephone-based CP@clinic program adaptation during the pandemic. METHODS: Community paramedics delivered CP@clinic via telephone to residents of 36 social housing buildings. They conducted screening for COVID-19, emergency preparedness, and social health factors. Community paramedics provided education on staying safe, self-isolating, self-monitoring, and preparing for pandemics using governmental infographics. Descriptive analysis was conducted on assessments completed between March and June 2020. Paramedic documentation was thematically analyzed to identify common themes. RESULTS: All 191 participants had ≥ 1 telephone visit and 34.6% had ≥ 2 telephone visits, 82.8% were aged 65 years and older, 30.9% had internet access, and 57.9% had cable TV (limiting exposure to COVID-19 information). The CP@clinic program infrastructure provided a platform for paramedics to swiftly contact many vulnerable older adults to screen for COVID-19, educate on safe practices, and facilitate healthcare access. One-quarter of participants screened positive for social isolation. Thematic analysis described participants' experiences adapting to daily life in the pandemic, emotional experience during the pandemic, and paramedics supporting participants during the pandemic. CONCLUSIONS: Public health interventions are needed for low-income or hard-to-reach older adults, especially during pandemics. Community paramedics were able to support vulnerable older adults living in social housing through the unique CP@clinic adaptation. This innovative program delivery increased vulnerable populations' access to public health services and information at a time of great health need.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".