Identification of Key Factors for Optimized Health Care Services: Protocol for a Multiphase Study of the Dubai Vaccination Campaign
Bibliographic record
Abstract
BACKGROUND: Mass vaccination of the global population against the novel COVID-19 outbreak posed multiple challenges, including effectively administering millions of doses in a short period of time while ensuring public safety and accessibility. The government of Dubai launched a mass campaign in December 2020 to vaccinate all its citizens and residents, targeting the population aged >18 years against COVID-19. The vaccination campaign involved a transformation of multiple commercial spaces into mass vaccination centers across the city of Dubai, the largest of which was the Dubai One Central (DOC) vaccination center. It was operational between January 17, 2021, and 27 January 27, 2022. OBJECTIVE: The multiphase research study aims to empirically explore the opinions of multiple health care stakeholders, elicit the key success factors that can influence the effective delivery of emergency health care services such as a COVID-19 mass vaccination center, and explore how these factors relate to one another. METHODS: To understand more about the operations of the DOC vaccination center, the study follows a multiphase design divided into 2 phases. The study is being conducted by the Institute for Excellence in Health Professions Education at Mohammed Bin Rashid University of Medicine and Health Sciences between December 2021 and January 2023. To elicit the key success factors that contributed to the vaccination campaign administered at DOC, the research team conducted 30 semistructured interviews (SSIs) with a sample of staff and volunteers who worked at the DOC vaccination center. Stratified random sampling was used to select the participants, and the interview cohort included representatives from the management team, team leaders, the administration and registration team, vaccinators, and volunteers. A total of 103 people were invited to take part in the research study, and 30 agreed to participate in the SSIs. To validate the participation of various stakeholders, phase 2 will analytically investigate one's subjectivity through Q-methodology and empirically investigate the opinions obtained from the research participants during phase 1. RESULTS: As of July 2022, 30 SSIs were conducted with the research participants. CONCLUSIONS: The study will provide a comprehensive 2-phase approach to obtaining the key success factors that can influence the delivery of high-quality health care services such as emergency services launched during a global pandemic. The study's findings will be translated into key factors that could support designing future health care services utilizing evidence-based practice. In line with future plans, a study will use data, collected through the DOC vaccination center, to develop a simulation model outlining the process of the customer journey and center workflow. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42278.
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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 | 0.063 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.019 |
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".