Using Social Media as a Survey Recruitment Strategy for Post-Secondary Students During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic rapidly forced Canadian post-secondary students into remote learning methods, with potential implications on their academic success and health. In recent years, the use of social media to promote research participation and as a strategy for communicating health messages has become increasingly popular. To better understand how the pandemic has impacted this population, we used social media platforms to recruit students to participate in a national bilingual COVID-19 Health Literacy Survey. The purpose of the survey was to assess the health literacy levels and online information-seeking behaviors of post-secondary students in relation to the coronavirus. This paper outlines the social media recruitment strategies used for promoting participation in the survey among Canadian post-secondary students during the pandemic. Facebook, Twitter, and Instagram accounts were created to promote the online survey. The objective of this paper is to examine the use of Instagram, Facebook, and Twitter as survey recruitment strategies tailored to students. Data analytics from these platforms were analyzed using descriptive statistics. We found that the most commonly used platform for survey dissemination was Twitter, with 64800 total impressions recorded over 3 months. The use of social media as a survey recruitment strategy showed promise in the current context of COVID-19 where many students are participating in online learning and for a study population that actively uses these platforms to seek out information.
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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 | 0.045 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".