Improving COVID-19 Vaccine Uptake in Saskatchewan, Canada: A Developmental Evaluation Approach
Bibliographic record
Abstract
Background The Developmental Evaluation of a COVID-19 vaccination program was an early response to assess a complex emergent mass vaccination program to support learning and adaptation. Objective The primary objective of a multi-disciplinary team of researcher-evaluators was to facilitate organizational learning among key stakeholders to improve decision-making and increase vaccine uptake in Saskatchewan, Canada. Methods Aligned with the Developmental Evaluation approach, data collection was rooted in adjustment and flexibility to meet the evolving needs of the vaccination program. Data were primarily collected using meeting observations and program documentation. As the program progressed, the data collection was adjusted, and two surveys were conducted targeting COVID-19 vaccine recipients and vaccine immunizers. Data were analyzed iteratively in consultation with stakeholders. Results Nine feedback reports were generated over a nine-month evaluation period. Seven reports highlighted meeting observation results that revealed the program issues, probable causes, and implications. The evolving issues ranged from vaccine shortage, delay, and supply fluctuation to inter-organizational miscommunication and vaccine hesitancy. Two reports were produced from survey findings to delve into the persistent issue of vaccine hesitancy. Conclusion Effective solutions to complex issues of Saskatchewan’s COVID-19 mass immunization require a systems approach based on new ways of thinking and collective decision-making.
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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 | 0.049 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".