Affective state of people suffering from long covid and associated factors. Cross-sectional descriptive study
Bibliographic record
Abstract
Introduction The “Post-COVID Syndrome” affects approximately 10% of people who have been infected with Covid-19. These people have a physical and mental impact. Objectives The objective of this study is to analyze factors related to poorer mental health in these patients from primary health care. Methods Cross-sectional study. The study population was post-COVID-19 patients aged 18 years or older and treated by Primary Health Care (PHC). The main variable was Affective state through the Hospital Anxiety and Depression Scale (HADS) questionnaire. The rest of the variables were: Socio-demographic variables, number of residual symptoms, cognitive using the Montreal Cognitive Assessment (MoCA), physical functioning variable will be measured by Sit to Stand Test and Sleep quality through the Insomnia Severity Index (ISI). A bivariate analysis and also a lineal multivariate model were developed. Ethics approval was granted by the Clinical Research Ethics Committee of Aragón (PI21/139 and PI21/454). Results A total of 100 individuals participated, of whom, 80 were women and 20 were men. The median scores in HADS was 16 and the interquartile range was 12. Multilevel analysis shows that better physical functioning (sit to stand test) and worse sleep quality (Insomnia severity index) are predictors of worse affective state. The models explain 36.5% of the HADS variance. Conclusions It is relevant to take account these variables in the treatment of the affective state of patients with long covid. Disclosure of Interest None Declared
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".