Leveraging Canadian healthcare worker volunteers to address COVID-19 vaccine misinformation on Facebook: A qualitative program evaluation (Preprint)
Bibliographic record
Abstract
BACKGROUND Social media is currently serving 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. OBJECTIVE (1) Train and deploy a group of healthcare workers and student volunteers to address anti-vaccine sentiment on Facebook; and, (2) Evaluate the intervention through semi-structured interviews to determine lessons learned and suggestions for future initiatives to address misinformation online METHODS The project utilized volunteers to address vaccine-hesitant comments on Facebook (Met Platforms Inc., Menlo Park, California), with the overall goal of empowering healthcare professionals to engage with vaccine-hesitant individuals online to counteract the spread of vaccine misinformation. Eligible participants included healthcare workers and students in healthcare-related disciplines were recruited through social media and email advertising campaigns by the University of Calgary, School of Nursing contact list. Informational training sessions on Zoom with a duration of 30-minutes followed, to better equip volunteers with the ability to utilize their working knowledge of health communication and behaviour change to correct online misinformation. During the deployment of volunteers, they 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 in the project were provided with working knowledge of health communication, behaviour change, and correct misinformation through the informational training sessions. Qualitative evaluation in the form of interviews were used to examine participant experiences, where it was found that volunteers felt that they were adequately equipped to engage in vaccine conversations both online and in healthcare settings. Overall, the project has addressed vaccine hesitancy and valuable insights into the relationship between public engagement and communication in the era of digital interconnectedness. RESULTS Following the evaluative interview discussions, three main themes emerged regarding the project’s format and training model, the factors motivating volunteers to participate, and overall experiences tackling misinformation on an online 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. Finally, 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 healthcare professionals and students while engaging in vaccine-related conversations online. Immediate challenges associated with the COVID-19 pandemic, including combating misinformation and promotion of vaccination were addressed, which has resulted in a more reliable groundwork for shaping future public health communication strategies. To enhance both participant satisfaction and compliance, future interventions should consider utilizing a self-paced format, flexible hours, and highlight the vitality of healthcare 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 | 0.042 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".