Leveraging Canadian Health Care Worker Volunteers to Address COVID-19 Vaccine Misinformation on Facebook: Qualitative Program Evaluation Study
Bibliographic record
Abstract
Background: Social media serves as a tool for increased digital interconnectedness and has resulted in playing an instrumental role in sharing health-related information with a wide audience. In conjunction with the vast availability of information, there has been a rapid spread of misinformation, leading to public mistrust, safety concerns, and discrimination. The COVID-19 pandemic has amplified the threat of misinformation resulting in detrimental health outcomes due to individuals becoming fatigued with COVID-19 health guidance. Although vaccinations are the key to combating COVID-19, the overwhelming amount of misinformation has resulted in diminished vaccine acceptance. Unlabelled: This study aims to (1) train and deploy a group of health care workers and student volunteers to address antivaccine sentiment on Facebook (Meta Platforms, Inc) and (2) evaluate the intervention through semistructured interviews to determine lessons learned and suggestions for future initiatives to address internet-based misinformation online. Methods: The project used volunteers to address vaccine-hesitant comments on Facebook (Meta Platforms, Inc), with the overall goal of empowering health care professionals to counteract the spread of vaccine misinformation. Eligible participants included health care workers and students in health care-related disciplines recruited through social media and email advertising campaigns. Informational training sessions followed, to better equip volunteers with the ability to use their working knowledge of health communication and behavior change to correct web-based misinformation. The volunteers were provided a file containing Facebook posts that discussed COVID-19 vaccines to act as a starting point for leaving or responding to comments that spread vaccine misinformation. Participants were provided with working knowledge of health communication, behavior change, and correct misinformation through the informational training sessions. Qualitative evaluation in the form of interviews was used to examine participant experiences. Results: Three main themes emerged regarding the project's format and training model, the factors motivating volunteers to participate, and overall experiences tackling misinformation on a social media platform. The first theme showcased that the training format was effective due to its use of interactive components and overall flexibility, resulting in it being well received by volunteers. The second identified theme highlighted that a main driving factor for participation included a balance of professional development and societal good. The third theme revealed that the volunteers' experiences in interacting with the public revealed a rich tapestry of emotions and perspectives, where vaccine hesitancy is interconnected with emotional responses and personal beliefs. Conclusions: The Informed Choice Project provided an opportunity to increase self-efficacy and confidence for more than a dozen health care professionals and students while engaging in vaccine-related conversations on social media. To enhance both participant satisfaction and compliance, future interventions should consider using a self-paced format, flexible hours, and highlight the vitality of health care professionals as key advocates for trusted sources of information for the public.
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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 | 0.037 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".