Asian Adolescent Experiences of Health-Related Stigma During the Covid-19 Pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic, which took place between March 2020 and May 2023, has significantly impacted people’s lives. Individuals with COVID-19, like other infectious diseases, are at risk for stigmatization related to their health status. As a group, Asian Canadians faced increased racism and discrimination during the pandemic in relation to prejudicial attitudes on spread and contagion of the disease. \n\nMethods: This study used Interpretive Descriptive methodology to explore the experiences of Asian Canadian adolescents who have had COVID-19 through one-on-one interviews virtually and in person. Prior to the interview, participants filled out informed consent forms and demographic forms to obtain background information. The interviews were conducted between February 2023 and August 2023. Afterwards, the data was analyzed into codes and themes. \n\nResults: The sample consisted of eleven Asian Canadian adolescents between the ages of 16 and 19-years old living in Ontario. Seven participants identified as female, three identified as male and one participant identified as non-binary. The participants reported facing stigmatization related to their positive COVID-19 status, their Asian identity, and their age. The findings were organized using the Health Stigma and Discrimination Framework (HSDF), which conceptualizes health-related stigma into drivers and facilitators, intersecting stigmas, manifestations, and outcomes. Drivers and facilitators included attitudes about people with COVID-19, Anti-Asian racism, the media and social media and time of infection. The intersecting stigmas were ageism and racism. Manifestations included negative reactions from others, anti-Asian experiences, and internalized stigma. Lastly, outcomes included the mental health consequences of racism and worsened academic performance. Challenges while isolating included difficulties keeping up with schoolwork and isolation. Positive mediators included peer and familial support and being able to access information about COVID-19\n\nConclusions: Asian Canadian adolescents faced complex experiences and challenges during the pandemic related to their intersecting identities including stigmatization due to their COVID-19 status, as well as racism due to their Asian background and ageism from older individuals. Understanding these experiences can better inform the services provided to this population and decrease the risks and effects of stigmatization.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".