A short version of the post-COVID-19 condition stigma questionnaire
Bibliographic record
Abstract
Objectives: The purpose of this study was to develop a short version of the 40-item Post-COVID-19 Condition Stigma Questionnaire (PCCSQ) while preserving its factor structure, reliability, and validity. The PCCSQ is a sound tool for assessing the discrimination experienced by people with a diagnosis of long covid, but a shorter version would be less demanding of respondents experiencing fatigue and brain fog and easier for clinicians and researchers to administer. Study design: This was an observational study. Methods: , we assembled 12 items that represented the factors and discriminated among participants with high and low levels of stigma. We administered the shorter questionnaire to 99 long covid patients and assessed several of its measurement properties. Results: The 12-item instrument maintains the 6-factor structure of long covid stigma, has a mean discrimination index of 0.40 (sd = 0.08; range 0.22-0.48), an internal consistency of α = 0.89, a split-half reliability of 0.86, and it correlates predictably with theoretically-related variables. Conclusions: The PCCSQ-12 is a feasible, reliable and valid means of assessing patients' experience of long covid stigma.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".