Prospective sampling bias in COVID-19 recruitment methods: experimental evidence from a national randomized survey testing recruitment materials
Bibliographic record
Abstract
BACKGROUND: In the context of the COVID-19 pandemic, social science research has required recruiting many prospective participants. Many researchers have explicitly taken advantage of widespread public interest in COVID-19 to advertise their studies. Leveraging this interest, however, risks creating unrepresentative samples due to differential interest in the topic. In this study, we investigate the design of survey recruitment materials with respect to the views of resultant participants. METHODS: Within a pan-Canadian survey (stratified random mail sampling, n = 1969), the design of recruitment invitations to prospective respondents was experimentally varied, with some prospective respondents receiving COVID-specific recruitment messages and others receiving more general recruitment messages (described as research about health and health policy). All respondents participated, however, in the same survey, allowing comparison of both demographic and attitudinal features between these groups. RESULTS: Respondents recruited via COVID-19 specific postcards were more likely to agree that COVID-19 is serious and believe that they were likely to contract COVID-19 compared to non-COVID respondents (odds = 0.71, p = 0.04; odds = 0.74, p = 0.03 respectively; comparing health to COVID-19 framed respondents). COVID-19 specific respondents were more likely to disagree that the COVID-19 threat was exaggerated compared to the non-COVID survey respondents (odds = 1.44, p = 0.02). CONCLUSIONS: COVID-19 recruitment framing garnered a higher response rate, as well as a sample with greater concern about coronavirus risks and impacts than respondents who received more neutrally framed recruitment materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.592 | 0.706 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".