An Organizational Assessment of 34 Home Delivered Meals Programs that Engaged and Assisted Homebound Individuals With Obtaining the COVID-19 Vaccine During the Pandemic
Bibliographic record
Abstract
Vaccinating homebound individuals during the COVID-19 pandemic presented several challenges, including time and cost of engaging this group. In Los Angeles County, the departments of Public Health and Aging and Disabilities turned to home delivered meals programs (HDMs) for help with this public health priority. A mixed-method organizational assessment of 34 HDMs was conducted during March–April 2022 to describe these efforts. Most HDMs were nonprofit (67.6%) and had <25 staff (58.8%). Overall, they served a large catchment area before and during COVID-19, providing services to an estimated total of 24,995 clients/week and delivering 19,511 meals/day. A majority (82.4%) reported engaging their clients to facilitate COVID-19 vaccinations. As of early 2022, <6% of these HDMs’ homebound clients were unvaccinated. These programs’ efforts to assist older individuals who were homebound during the pandemic represent a potentially underutilized model of public-nonprofit/not-for-profit partnership for improving vaccine delivery and uptake in this hard-to-reach population.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".