Persecutory ideation and anomalous perceptual experiences in the context of the COVID-19 outbreak in France: what's left one month later?
Bibliographic record
Abstract
Aims: Beyond the effects of the coronavirus pandemic on the public's health, the length of lockdown and its possible psychological impacts on populations around the world is heavily debated. However, the consequences of lockdown on psychotic symptoms have not yet been investigated. Methods: An online survey was run from April 13 to May 11, 2020; a total of 728 French subjects from the general population participated. We assessed the perceived impact of the COVID-19 outbreak, length of self-isolation, diagnosis/symptoms/hospitalisation related to the COVID-19 (oneself and family). Paranoid ideations and anomalous perceptual perceptions were assessed via the Paranoia Scale and the Cardiff Anomalous Perceptions Scale. Measures of negative affect, loneliness, sleep difficulties, jumping to conclusion bias, emotion regulation, and perseverative thinking were also included. Results: Final regression model for paranoia indicated that socio-demographic variables, loneliness, cognitive bias, anxiety, repetitive thoughts and hallucinations were associated with paranoia (R2 = 0.43). For hallucinations, clinical variables as well as the quality of sleep, behavioural activation, repetitive thoughts, and paranoia were associated with hallucinations in our sample (R2 = 0.27). Neither length of self-isolation nor the perceived impact of the COVID-19 pandemic were associated with psychotic experiences in the final models. Conclusions: No evidence was found between a significant impact of self-isolation on psychotic symptoms in the general population in France one month after the lockdown. It nevertheless confirms the preeminent role of several factors previously described in the maintenance and development of psychotic symptoms in the context of a pandemic and lockdown measures.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".