Multi-Disciplinary Design and Implementation of a Mass Vaccination Clinic Mobile Application to Support Decision-Making
Bibliographic record
Abstract
Mass vaccination clinics are complex systems that combine professionals who do not typically work together. Coordinating vaccine preparation and patient intake is critically important to maintain patient flow equilibrium, requiring continuous communication and shared decision-making to reduce vaccine waste. OBJECTIVES: (1) To develop a mobile application (app) that can address the information needs of vaccination clinic stakeholders for end-of-day doses decision-making in mass immunization settings; and (2) to understand usability and clinical implementation among multi-disciplinary users. METHODS: Contextual inquiry guided 71.5 hours of observations to inform design characteristics. Rapid iterative testing and evaluation were performed to validate and improve the design. Usability and integration were evaluated through observations, interviews, and the system usability scale. RESULTS: Designing the app required consolidating contextual factors to support information and workload needs. Twenty-four participants used the app at four clinics who reported its effectiveness in reducing stress and improving communication efficiency and satisfaction. They also discussed positive workflow changes and design recommendations to improve its usefulness. The average system usability score was 87 (n = 22). DISCUSSION: There is significant potential for mobile apps to improve workflow efficiencies for information sharing and decision-making in vaccination clinics when designed for established cultures and usability, thereby providing frontline workers with greater time to focus on patient care and immunization needs. However, designing and implementing digital systems for dynamic settings is challenging when healthcare teams constantly adapt to evolving complexities. System-level barriers to adoption require further investigation. Future research should explore the implementation of the app within global contexts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".