Portrait of Mental Health Identified by People with the Post-Covid Syndrome
Bibliographic record
Abstract
Abstract Aims The Post-COVID-19 syndrome (PCS) represents an epidemic within the COVID-19 pandemic, with potentially serious consequences for affected individuals, the healthcare system, and society at large. Facing a new and poorly understood health condition, this study aimed to produce a patient-centered understanding of mental health symptom patterns, functional impact, and intervention priorities. Methods A cross-sectional analysis of the first 414 participants in a longitudinal study recruited over a 5- month from September 2022 to January 2023 was carried out involving people from Quebec who self-identified as having symptoms of PCS. People were asked to name areas of their mental health affected by PCS using the structure of the Patient Generated Index (PGI), an individualized measure suited to eliciting the most frequent and most bothersome symptoms. The PGI was supplemented with a set of patient-reported outcome measures across the rubrics of the Wilson- Cleary model. The text threads from the PGI were grouped into topics using BERTopic analysis. Results Twenty topics were identified from 818 text threads referring to PCS mental health symptoms nominated using the PGI format. 35% of threads were identified as relating to anxiety, discussed in terms of five topics: generalized/social anxiety, fear/worry, post-traumatic stress, panic, and nervous. 29% of threads were identified as relating to low mood, represented by five topics: depression, discouragement, emotional distress, sadness, and loneliness. A cognitive domain (22% of threads) was covered by four topics referring to concentration, memory, brain fog, and mental fatigue. Topics related to frustration, anger, irritability. and mood swings (7%) were considered as one domain and there were separate topics related to motivation, insomnia, and isolation. Conclusion This novel method of digital transformation of unstructured text data uncovered different ways in which people think about classical mental health domains. This information could be used to evaluate the extent to which existing measures cover the content identified by people with PCS or to justify the development of a new measure of the mental health impact of PCS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".