Pain, Sleep Disturbance and Smoking Among Patients with Covid-19 Presenting to the Emergency Department
Bibliographic record
Abstract
AİM: The study aimed to determine the pain, sleep disturbance, and smoking among patients with Covid-19 who were presented to emergency departments. METHOD: This descriptive research was conducted between November 2020 and December 2021. The study population comprised 400 patients with COVID-19 who were presented to emergency departments at Ataturk University Research Hospital and Erzurum City Hospital and who agreed to participate in the study. The data were collected by the researcher via face-to-face interviews. Personal Information Form, Fagerström Test for Nicotine Dependence, Insomnia Severity Index, and McGill Pain Scale Short Form were used to collect the data. Descriptive statistics were presented as number, percentage, mean, and standard deviation. Parametric and nonparametric methods (t-test, Kruskal-Wallis Variance, Mann-Whitney U test, and Analysis of Variance (ANOVA) were used to compare variables between the groups. Ethical approval was obtained from the relevant authority prior to data collection and oral consent was obtained from all patients. RESULTS: It was determined that 52.5% of the patients were smokers; 24% of the smokers reported a decrease in smoking after being diagnosed with COVID-19. Nicotine addiction was found to be higher in men, tradesmen, and patients aged 55-64 years. McGill pain scale emotional sub-dimension scores were higher in women, whereas the sensory sub-dimension scores were higher in married patients. McGill pain scale total scores were higher in women, unemployed patients, and those with chronic diseases. Insomnia severity index was higher in women, smokers, and patients in the age group of 65-75 years. CONCLUSION: According to the results of the present study, pain, smoking, and sleep disorders in patients diagnosed with COVID-19 were affected by socio-demographic characteristics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".