COVID-19 Vaccination in Home Health and Hospice: Barriers to Vaccination and Results From a Home Vaccination Program
Bibliographic record
Abstract
Abstract Little is known about vaccination rates in home health and hospice populations. Results draw upon two separate data sources from The Visiting Nurse Association Health Group (VNAHG). Among VNAHG patients surveyed between February 2 and March 1, 202, 24% had received at least one COVID-19 vaccine. Among vaccinated patients, roughly one quarter did not travel to get the vaccine (received inpatient vaccination). They mostly traveled by car (88%), and 70% received help from a family member. Of patients who had not received a vaccine (76%), 81% were pursuing or planning to pursue obtaining a vaccine. Additionally, of those not pursuing a vaccine, 30% indicated it was because they could not get to a vaccine site. 44% of patients in the VNAHG “in home” vaccination pilot were bedbound, and 100% of patients had ambulation difficulties that make it impossible for them to leave home. All (100%) had a health care provider(s) recommended they get the vaccine. Only 38% have internet access. A quarter tried to call to schedule a vaccine, but only one was able to speak to someone. 40% of the patients attempted to get a COVID-19 vaccine prior to enrollment in the program. Most patients (81%) did not have someone available to assist with their transportation to get vaccinated, and most indicated difficulty securing an appointment. Many indicated severe traveling difficulties (requiring oxygen, needing ambulance transport). These findings highlight the high barriers for homebound patients, and the need and value of clinicians traveling to provide in-home vaccines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".