Nova Scotia Strong: why communities joined to embrace COVID-19 public health measures
Bibliographic record
Abstract
OBJECTIVE: To explore Nova Scotian experiences, barriers, and facilitators associated with pandemic public health measures (PHM), including COVID-19 vaccination. METHODS: We conducted semi-structured, individual interviews with Nova Scotians between May and August 2021, during the third wave of COVID-19 cases and provincial lockdown. Participants were recruited across the province from three sectors: decision makers, community leaders, and community members using purposive and snowball sampling. Direct content analysis and thematic analysis were used to identify key themes via the Theoretical Domains Framework. RESULTS: The experiences of 30 Nova Scotian interviewees clustered around four themes: Communication of PHM, Responsibly Observing PHM: A Community Coming Together, Navigating PHM, and Vaccine Confidence & Hesitancy. Consistent communication of PHM through briefings with the chief medical officer of health and provincial channels reduced misinformation and encouraged PHM compliance. While adherence was high throughout the province, inconsistent enforcement of these measures proved challenging to individuals navigating PHMs. A high level of COVID-19 vaccine confidence and acceptance was identified, and a strong sense of provincial pride prevailed in keeping COVID-19 numbers and transmission low. CONCLUSION: This study provides insights into Nova Scotians' unique experiences with COVID-19 PHM. Provincial public health experts and government leaders communicated PHM with various levels of success, Nova Scotia Strong, a sentiment of unity and communitarianism that sprang from public response to tragic events. Future work should aim to include under-represented communities to facilitate broader inclusion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".