British Columbia’s COVID-19 surveys on population experiences, action, and knowledge (SPEAK): methods and key findings from two large cross-sectional online surveys
Bibliographic record
Abstract
OBJECTIVES: To describe the methodology and key findings of British Columbia's (BC) COVID-19 SPEAK surveys, developed to understand the experiences, knowledge, and impact of the COVID-19 pandemic on British Columbians. METHODS: Two province-wide, cross-sectional, web-based population health surveys were conducted one year apart (May 2020 and April/May 2021). Questions were drawn from validated sources grounded within the social determinants of health to assess COVID-19 testing and prevention; mental and physical health; risk and protective factors; and healthcare, social, and economic impacts during the pandemic. Quota-based non-probability sampling by geography was applied to recruit a representative sample aged 18 years and older. Recruitment included strategic outreach and longitudinal follow-up of a subgroup of respondents from round one to round two. Post-collection weighting using Census data by age, sex, education, ethnicity, and geography was conducted. RESULTS: Participants included 394,382 and 188,561 British Columbians for the first and second surveys, respectively, including a longitudinal subgroup of 141,728. Key findings showed that societal impacts, both early in the pandemic and one year later, were inequitably distributed. Families with children, young adults, and people from lower socioeconomic backgrounds have been most impacted. Significant negative impacts on mental health and stress and a deterioration in protective resiliency factors were found. CONCLUSION: These population health surveys consisting of two large cross-sectional samples provided valuable insight into the impacts and experiences of British Columbians early in the pandemic and one year later. Timely, actionable data informed several high-priority public health areas during BC's response to the COVID-19 pandemic.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".