Benefits and challenges of high-density microarray patches for vaccination among older adults: A qualitative study
Bibliographic record
Abstract
Objectives Microarray Patches (MAPs) deliver vaccines to the upper dermis and epidermis, rich in immune cells. This study explored the perceived safety, usability, and acceptability of High-Density Microarray Patches (HD-MAPs) among older adults aged 50+. Methods This was a single-centre, single-arm, open-label study using excipient-coated HD-MAPs. A trained user administered two HD-MAPs to each participant’s dominant arm, and participants self-administered to their non-dominant arm. Semi-structured interviews were conducted on days 0 and 28. Thematic analysis was used to explore participant experiences. Results Forty-four older adults were recruited. Themes explored the benefits and challenges of HD-MAPs. Benefits included (1) mass distribution and administration, (2) reduced healthcare burden, and (3) convenience, particularly in low-resource settings due to thermostability and potential for self-administration. Participants felt that use of trained users and self-administration could reduce burden on healthcare resources. Challenges highlighted were (1) safety of unsupervised use, (2) proof of vaccination, and (3) user confidence and cost. Concerns about adverse events and correct dosage were raised, though participants felt reassured by the sensation, applicator sound, and mark after application. Conclusion HD-MAPs may ease healthcare burdens and improve convenience and acceptability among older adults, offering an alternative to needles and syringes, especially for vulnerable populations.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".