“Flying a plane and building it at the same time”: Lessons learned from the dynamic implementation of mass vaccination clinics in the Region of Waterloo, Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Vaccination plays a critical role during pandemics, and mass vaccination clinics are often an imperative public health measure. These clinics usually consist of multi-disciplinary teams, which can pose significant coordination challenges, yet also present an opportunity for collectively contributing towards mitigating the impact of infection within communities. This study explores the coordination dynamics of the Region of Waterloo's coronavirus disease of 2019 (COVID-19) mass vaccination clinics in Ontario, Canada, between July 2021 and April 2022. METHODS: This qualitative study included 16 purposively selected participants working in mass vaccination clinics. Participants were individually interviewed for 40-60 min. An inductive and iterative thematic analysis was undertaken, including open coding, grouping, labelling, regrouping and making sense of the themes. RESULTS: Three interrelated themes were created: (1) unpredictable work environment, which was comprised of changing clinic processes and the impact of clinic adjustments to the running of the clinics; (2) clinic cohesion challenges, which included staff role disparities, limited job preparation and clinic system silos; and (3) adaptable and supportive work environment, which was comprised of staff adaptability, dispositional flexibility and a supportive work environment. While the first two themes created a precarious situation in the clinics, the third countered it, leading to a largely successful clinic implementation. CONCLUSIONS: The rapid evolution and high transmissibility of COVID-19 in communities required a public health response that felt like flying and building a plane simultaneously - a seemingly impossible yet necessary task. However, an adaptable and supportive work environment was critical for establishing an atmosphere that can overcome challenges from a constantly changing pandemic and the guidance of public health officials. Such lessons gained from understanding the dynamic experiences in mass vaccination clinics are essential for improving the development and operation of future immunization campaigns.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".