Developing a patient-oriented realist evaluation for COVID-19 vaccine implementation in Saskatchewan: a methodologic framework
Bibliographic record
Abstract
BACKGROUND: There is an urgent need to inform decision-making and safe delivery of vaccines in a timely manner. Our objective is to describe the methods we used to perform a patient-oriented realist evaluation of COVID-19 vaccination implementation in Saskatchewan, Canada, in order to understand the underlying mechanisms and contexts of vaccination implementation and vaccine uptake. METHODS: This methodology paper describes a patient-oriented, realist, mixed-method evaluation to assess COVID-19 vaccination implementation in Regina, Saskatoon and Prince Albert, Saskatchewan. The study comprised 3 iterative phases guided by Realist And Meta-narrative Evidence Synthesis: Evolving Standards II (RAMESES II). In phase 1 (January-February 2021), we developed the initial program theory, in phase 2 (March-May 2021), we tested and refined the initial program theory, and in phase 3 (June-July 2021), we established the final program theory. Three patient and family partners with different backgrounds and experiences were selected purposively from various locations (urban and rural) in Saskatchewan to engage collaboratively in the evaluation. Data analysis and synthesis occurred at all 3 phases of the project. We analysed qualitative data from phases 2 and 3 using a "retroductive" approach. We used quantitative data to compare outcomes from the 3 sites. INTERPRETATION: This protocol describes how we developed a final program theory for COVID-19 vaccination implementation with patient and family partners to show for whom, under what circumstances, how and why Saskatchewan's COVID-19 vaccination program has led to vaccine uptake. With patient and family partners' engagement, the evaluation findings will be shared with the Saskatchewan Health Authority and provincial government policy-makers and communications departments, published in peer-reviewed journals, presented at provincial or national conferences, and disseminated through any additional media identified by the patient and family partners.
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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 | 0.514 | 0.375 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".