Canadians’ opinions towards COVID-19 data-sharing: a national cross-sectional survey
Bibliographic record
Abstract
OBJECTIVES: COVID-19 research has significantly contributed to pandemic response and the enhancement of public health capacity. COVID-19 data collected by provincial/territorial health authorities in Canada are valuable for research advancement yet not readily available to the public, including researchers. To inform developments in public health data-sharing in Canada, we explored Canadians' opinions of public health authorities sharing deidentified individual-level COVID-19 data publicly. DESIGN/SETTING/INTERVENTIONS/OUTCOMES: A national cross-sectional survey was administered in Canada in March 2022, assessing Canadians' opinions on publicly sharing COVID-19 datatypes. Market research firm Léger was employed for recruitment and data collection. PARTICIPANTS: Anyone greater than or equal to 18 years and currently living in Canada. RESULTS: 4981 participants completed the survey with a 92.3% response rate. 79.7% were supportive of provincial/territorial authorities publicly sharing deidentified COVID-19 data, while 20.3% were hesitant/averse/unsure. Datatypes most supported for being shared publicly were symptoms (83.0% in support), geographical region (82.6%) and COVID-19 vaccination status (81.7%). Datatypes with the most aversion were employment sector (27.4% averse), postal area (26.7%) and international travel history (19.7%). Generally supportive Canadians were characterised as being ≥50 years, with higher education, and being vaccinated against COVID-19 at least once. Vaccination status was the most influential predictor of data-sharing opinion, with respondents who were ever vaccinated being 4.20 times more likely (95% CI 3.21 to 5.48, p=0.000) to be generally supportive of data-sharing than those unvaccinated. CONCLUSIONS: These findings suggest that the Canadian public is generally favourable to deidentified data-sharing. Identifying factors that are likely to improve attitudes towards data-sharing are useful to stakeholders involved in data-sharing initiatives, such as public health agencies, in informing the development of public health communication and data-sharing policies. As Canada progresses through the COVID-19 pandemic, and with limited testing and reporting of COVID-19 data, it is essential to improve deidentified data-sharing given the public's general support for these efforts.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".