Effectiveness, acceptability, and potential of lay student vaccinators to improve vaccine delivery
Bibliographic record
Abstract
SETTING: Task sharing can fill health workforce gaps, improve access to care, and enhance health equity by redistributing health services to providers with less training. We report learnings from a demonstration project designed to assess whether lay student vaccinators can support community immunizations. INTERVENTION: Between July 2022 and February 2023, 27 undergraduate and graduate students were recruited from the University of Toronto Emergency First Responders organization and operated 11 immunization clinics under professional supervision. Medical directives, supported with online and in-person training, enabled lay providers to administer and document vaccinations when supervised by nurses, physicians, or pharmacists. Participants were invited to complete a voluntary online survey to comment on their experience. OUTCOMES: Lay providers administered 293 influenza and COVID-19 vaccines without adverse events. A total of 141 participants (122 patients, 17 lay vaccinators, 1 nurse, and 1 physician) responded to our survey. More than 80% of patients strongly agreed to feeling safe and comfortable with lay providers administering vaccines under supervision, had no concerns with lay vaccinators, and would attend another lay vaccinator clinic. Content and thematic analysis of open-text responses revealed predominantly positive experiences, with themes about excellent vaccinators, organized and efficient clinics, and the importance of training, communication, and access to regulated professionals. The responding providers expressed comfort working in collaborative immunization teams. IMPLICATIONS: Lay student providers can deliver vaccines safely under a medical directive while potentially improving patient experiences. Rather than redeploying scarce professionals, task sharing strategies could position trained lay vaccinators to support immunizations, improve access, and foster community engagement.
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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".