COVID-19 Vaccine Uptake among Recipients in Saskatchewan: A Patient-Oriented Realist Evaluation
Bibliographic record
Abstract
Abstract Background: When the COVID-19 vaccination program started in Saskatchewan, Canada, there was a need to understand what worked or did not work during the vaccination pilot phase that took place in Regina, Saskatoon, and Prince Albert to plan for improved vaccine uptake. This evaluation study had three objectives: a) to document the vaccination implementation plan in the three pilot sites; b) to understand how, for whom, in which circumstances, and why the plan led to vaccine uptake from the perspectives of eligible vaccine recipients; and c) to establish program theories that could be adapted to multiple settings. Methods: We conducted a patient-oriented realist evaluation of the Saskatchewan’s vaccination pilot phase that happened from December 2020 to March 2021. The study comprised of three iterative phases, including developing initial program theories (IPTs) by reviewing literature as well as Saskatchewan’s COVID-19 vaccination delivery plan (phase one), testing the IPTs by conducting interviews with vaccine recipients (phase two), and developing final program theories (PTs) by refining the IPTs (phase three). Three patient and family partners were fully engaged at each phase. A retroductive approach was used to analyze qualitative data. Results: Virtual interviews were performed with six participants representing each group of eligible vaccine recipients (ICU/ED physicians, nurses, and healthcare workers; long-term care [LTC] managers and healthcare workers; and family members and care givers of LTC residents on behalf of LTC residents). In the three final PTs, 12 contextual factors and 14 casual mechanisms resulted in an intermediate outcome of vaccine willingness or hesitancy which then led to vaccine uptake as an outcome of interest. Communication (e.g., social media, internal and external sources of communication) and trust (e.g., in leadership and medical professionals), were the most prominent contextual factor and causal mechanism, respectively. Conclusions: Our final program theories displayed the complexity and interconnectedness of contexts and mechanisms. Some mechanisms were activated for some participants, and not for others, depending on their circumstances which consequently affected vaccine uptake. These findings suggest the need for more tailored strategies to address vaccine recipients’ specific needs and conditions.
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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 | 0.040 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".