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Record W4220808181 · doi:10.33137/utjph.v3i1.38126

Structural and Systematic Discrimination Driven Misinformation

2022· article· en· W4220808181 on OpenAlex
Abisha Yogaratnam

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueUniversity of Toronto Journal of Public Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsMisinformationHealth literacyPublic healthPublic relationsHealth equitySocial determinants of healthSocial distanceHealth communicationPolitical sciencePandemicSocial inequalityPopulationEconomic growthSociologyPsychologyInequalityMedicineHealth careEnvironmental healthCoronavirus disease 2019 (COVID-19)NursingEconomics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.046
GPT teacher head0.295
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it