Measuring the stigma of long COVID: principles and practices transferrable to other stigmatizing respiratory conditions
Bibliographic record
Abstract
Background: Our academic Post COVID-19 Condition (PCC) clinic in the Canadian city of Edmonton became operational in June 2020. Almost immediately, clinic patients began to relay stories consistent with stigmatization. Stigma has the potential to negatively impact the health of individuals, communities, and entire populations, and is now considered a social determinant of health. Aims and Objectives: We developed a 40-item instrument (the Post COVID-19 Condition Stigma Questionnaire, or PCCSQ) with which to quantify PCC-related stigma, and undertook a prospective cohort study to determine the reliability and validity of this novel tool. Results: Between May 2021 and May 2022, 198 patients were asked to participate. 83% consented; 76% completed useable surveys; 6 were excluded (no PCR confirmation of acute COVID-19; recruited prior to PCC diagnosis); 145 (73%) were included in the analysis. Reliability was > 0.90. Total Stigma Score (TSS) on the PCCSQ ranged from 40 – 174/200 (mean 103; SD 31). Individuals with increased TSS were found in all demographic subgroups. Increased PCC-related stigma was significantly associated with symptom burden, reduced self-esteem, reduced functional status, frailty, social isolation, loneliness, decreased quality of life, increased emergency department use, and unemployment due to disability. Conclusions: The PCCSQ is a reliable and valid tool with which to estimate PCC-related stigma. Our results are consistent with the Health Stigma and Discrimination Framework (Stangl, A et al. 2019), and share many similarities to the stigma arising from other respiratory conditions such asthma, COPD, interstitial lung disease, lung cancer, tuberculosis, etc.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".