Role satisfaction among community volunteers working in mass COVID-19 vaccination clinics, Waterloo Region, Canada
Bibliographic record
Abstract
INTRODUCTION: Unpaid community volunteers are a vital public health resource in times of crisis. In response to the COVID-19 pandemic, community volunteers were mobilized to support mass vaccination efforts in many countries. To have this group's continued engagement, it is essential to understand the community volunteer experience, including the opportunities and challenges they encounter and how these contribute to their role satisfaction. This qualitative study investigated the factors contributing to community volunteers' role satisfaction at COVID-19 mass vaccination clinics in the Region of Waterloo, Canada. METHODS: Qualitative data were analyzed from 20 volunteers (aged 48-79 years) who had worked at one of four COVID-19 vaccination clinics in the Region of Waterloo, Canada. Data were analyzed thematically using an inductive coding process followed by an iterative process of grouping and identifying linkages and relationships within the themes. RESULTS: Four interrelated themes were developed from the inductive analysis process. The theme of community volunteers feeling valued or disesteemed in their role depends on the interaction between the three themes of role description, role preparation, and clinic context. CONCLUSIONS: For volunteers in crises such as the COVID-19 pandemic, volunteer role satisfaction depends on how their contributions are valued, the clarity of their role descriptions, volunteer-specific training, and the sentiments of volunteers and staff within the clinic context. Greater role satisfaction can help with retention as volunteers become more resilient and adaptable to the complex dynamic circumstances of a crisis response. Activities such as training and materials development for role preparations should be explicitly planned and well-resourced, even in crisis/pandemic situations. Building clinic managers' or supervisors' skills in communication during crisis/pandemic situations and the skills for the creation of team cohesion are critical investment areas.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".