Bibliographic record
Abstract
Objective: The aim of the study; it is the investigation of pain status in healthcare workers who are at high risk after COVID-19. Methods: A total of 180 healthcare professionals aged 18-65 years who had COVID-19 and were not COVID-19 participated in the study. Data were collected between February and May 2021. During the data collection phase, the "Preliminary Evaluation Form" and the "McGill- Melzack pain Questionnaire (MPQ)", which includes demographic information and information about the COVID-19 situation, were used. Results: It was found that there was no statistically significant difference between the pain questionnaire scale scores between the groups with and without COVID-19 (p=.951). It was determined that the scores of the health technicians were statistically lower than the scores of the nurses-midwives (p=.022). It was found that pain scores did not differ statistically significantly according to gender (p=.947). It has been observed that deep pain is mostly defined in the upper back (36.6%) and lower back (34.4%) region, and superficial pain is defined in the neck (31.1%) region in those who had COVID-19. Conclusion: As a results; In healthcare workers who have had COVID-19, pain was most common in the waist and back regions. Pain did not differ in terms of gender in those who have COVID-19, however, nurses-midwives experienced more pain than health technicians.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".