Bibliographic record
Abstract
Introduction: While the world is focused on mitigating the impacts of COVID-19, the overwhelming need to focus on health literacy and communication is overlooked. As a pandemic to occur in a world of globalized communication, the spread of misinformation has presented crucial challenges in not only mitigating the transmission at the clinical level but has also impacted the way people have approached and experienced it. Misinformation during the pandemic has been heavily associated with the experiences of marginalized populations, and thus, can say, is driven by structural and systematic discrimination, which perpetuates mistrust and influences the perception. Through the Social Determinants of Health (SDOH) framework, this review aims to critically analyze the Public Health responses considering the social, cultural, and economic conditions that impact the inequity-driven experiences. Public health responses to the pandemic, especially during the first wave in Ontario, were heavily focused on social distancing, staying at home, and hygiene practices to lower the transmission of the virus. However, the interaction with these regulations varies depending on the different SDOH impacting the population and can directly cause the evolving mistrust in the messaging, as it may not be coherent with the experiences. The SDOH such as housing, income inequality, and language barriers, neighbourhood density, and cultural beliefs all play a role in the effectiveness of health literacy and communication and are already widely impacted by structural and systematic discrimination. Methods: A literature review was conducted to collect relevant data using the themes of Social Determinants of Health and misinformation during COVID-19 among marginalized populations. Of the articles, 25 articles were selected for when they matched the theme. Data was collected by a rigorous review of the selected articles. Results: The results of the search highlighted the impacts of misinformation during COVID-19 among individuals who were of lower socioeconomic status (SES), had diverse cultural backgrounds and were impacted by various social determinants. Findings suggested that communities who faced chronic systemic and structural barriers with inequitable social determinants, had higher exposure to misinformation. Discussion: The results of the literature review highlighted the need for an inclusive and upstream approach for public health responses. Much of the fear and disconnect caused by the misinformation of the pandemic is driven by the pre-existing structural and systematic discrimination. To better understand and address the harmful impacts, a more community-based approach is needed to tackle the stigma associated with the messaging of public health strategies. Individuals of marginalized populations need to feel more included to build a relationship where information provided will be perceived without mistrust and can lead to more accurate information consumption. If populations such as those of lower SES, feel that social distancing and essential travelling is the only way to prevent the risk of infection, then they may not have much trust in the system's response and may depend on misinformation provided by places of more familiarity as they are facing conditions that don’t allow them to follow the regulations. Health literacy/communication remains an impactful method in mitigating the concerns of misinformation and should be inclusive of the various intersections of the Social Determinants of Health at the community level. Only by including various cultural, social, and economic experiences can public health messaging reach populations.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".