Perception of COVID-19 by the Mentally ill Patients
Bibliographic record
Abstract
Background: Patients with mental illness constitute a population at risk of being easily infected by corona virus (COVID-19), and they can also spread the infection to others by neglecting protective measures. The confrontation of this pandemic depends above all on their adherence to barrier measures, largely affected by their knowledge, perception and practices. Objectives: This work aimed to assess the perception of COVID-19 in patients with mental illness; by conducting a survey on their knowledge, attitudes and practices. Methods: This is a descriptive cross-sectional study. 118 consultant patients with schizophrenia, bipolar disorder or chronic delusional disorder agreed to participate in the study. It was conducted at the psychiatric university department at Ibn Nafis hospital in Marrakech, during the month of October 2020. We used a pre-established questionnaire. The choice of patients was made given the frequency of perception disorders, cognitive deficit and inappropriate behaviors. Results: The mean age of the participants was 38 years. The majority were male (64%), single (56%), with a low level of education (32%). The patient’s average COVID-19 knowledge score was 9.27, suggesting an overall correct answer rate of 71% on this knowledge test. Almost a quarter of the patients did have a poor knowledge of COVID-19 (30%). Notably, more than half of the patients surveyed (62%) were not aware that there are asymptomatic forms of COVID-19 infection, and that there are no specific treatment or vaccine against COVID-19 currently. Two-thirds of patients (63%) considered COVID-19 infection to be a serious and dangerous illness, twenty-five patients (21%) had given no comments, nine patients (8%) believed that it was a conspiracy, six patients (5%) thought it was a devil and four patients (3%) thought it was a lie. In terms of attitudes, more than half of the patients surveyed had expressed their fear when thinking of COVID-19, and the majority of patients (90%) ...
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".