Investigating Post-COVID Condition in Systemically Underrepresented Communities: A Multiple Methods Thematic Approach within the Greater Toronto and Hamilton Area
Bibliographic record
Abstract
Post-COVID Condition (PCC) emerged as a significant public health concern shortly after COVID-19 (Lai et al., 2020). PCC is characterized by persistent symptoms, both flu and non-flu like, that continue weeks and months beyond an acute positive infection (Lai et al., 2020). Despite increasing rates of reported PCC cases, many individuals face challenges in receiving diagnosis and appropriate support (Lai et al., 2020). These challenges are further exacerbated by inequities experienced by marginalized populations who are often underrepresented in PCC research (Tsioutis et al., 2022). This study aims to explore the lived experiences of individuals with PCC with a particular focus on barriers to diagnosis and care, inequities in healthcare access, and the coping strategies employed to navigate daily life. Semi-structured qualitative interviews were conducted, and data was analysed thematically to identify recurring patterns across participants’ narratives. This study provided context surrounding the variability and uncertainty of symptoms, which often complicates recognition of PCC symptoms and delayed/prevented a formal diagnosis. Further, this study provided understanding surrounding systemic and structural barriers, including limited cultural and linguistic competent services, and social determinants such as housing and employment status which complicated access to care. Lastly, this study provided context within the role of social relationships, where participants navigated both support and scepticism from family and peers while managing stigma and cultural interpretations of their condition. This study contributes to the growing formulation of PCC literature by amplifying voices of systemically underrepresented communities and their experiences, offering insight into the ways PCC aligns with broader social determinants of health. Findings from this study highlight the need for healthcare systems to develop clear diagnostic guidelines, improve communication across healthcare, and expand accessible and equitable care pathways for diverse populations. While the scope of this study was limited by its sample size and geographic location, it offers valuable contributions by highlighting the lived experiences of individuals navigating PCC. Future research should continue to examine PCC through an equity lens, with emphasis on developing interventions that can improve recognition, support, and outcomes for those living with PCC.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".