Leveraging Community Support Services to Support an Integrated Health and Social System Response to COVID-19: A Mixed Methods Study
Bibliographic record
Abstract
The COVID-19 pandemic restricted access to health and social care for older adults. In response, a standardized, self-report instrument, the interRAI COVID-19 Vulnerability Screener (CVS), was developed. In collaboration with a community support service organization, this project aimed to evaluate a surveillance process to identify those at risk and triage them to health- or social-care services. A convergent, mixed methods design was used. Virtual focus groups were conducted with partner staff. The clinical, social, and functional characteristics of screened clients were analyzed descriptively. The mixed methods analysis generated implementation considerations. Participants successfully utilized the CVS to identify and support vulnerable clients who may have otherwise been overlooked. Most screened clients were not experiencing COVID-19 symptoms, yet had elevated mortality risk should they become infected, and were negatively impacted by social isolation. Findings support the use of the CVS by community support services during the pandemic or other disasters to identify those at risk due to frailty and social or economic vulnerability. Implementation requirements include a knowledgeable workforce, active facilitation, and incorporation of the CVS into existing workflows. Health- and social-care integration would be enhanced by the development of updated privacy policies and shared digital infrastructure.
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".