Prevalence and Impact on Job Performance of Primary Headache Among Medical and Paramedical Staff in the Emergency Department
Bibliographic record
Abstract
Background: The headache is one of the most common neurological disorders and ranks the third cause of years lost due to disability. So this study was conducted to identify the prevalence of headache and its impact on job performance in emergency department medical and paramedical staff. Methods: This was a cross-sectional study using self-administered questionnaire. A total of 308 medical and paramedical staff were selected randomly from emergency departments of Taif hospitals during the period from December 2016 to January 2017. Results: Three hundred eight staff participated in the study. One hundred fifty-eight (158, 51.3%) were males and 150 (48.7%) were females. One hundred thirty-two (132, 42.9%) were medical staff and 176 (57.1%) were paramedical staff. The last 3 months prevalence of headache among participants was 272 (88.3%) with statistical significant differences with physical activities (P = 0.008) and smoking (P = 0.020). Regarding the impact of headache, 86 (31.6%) had little to no impact and others had severe impact (74, 27.2%), remarkable impact (40, 14.7%) and some impact (72, 26.5%). There were statistical significant differences (P ≤ 0.05) between headache impact test and age, marital status, specialty, BMI, physical activities, smoking, headache duration, specialist consultation, medication use and frequency of absenteeism. Conclusion: The primary headache prevalence is very high among medical and paramedical staff in emergency departments. Its characteristics are almost meeting the diagnostic criteria of the tension-type headache. The impact of headache on job performance is little in most of the staff, but there is significant percent of those with severe impact. J Neurol Res. 2017;7(1-2):5-12 doi: https://doi.org/10.14740/jnr420e
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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 | 0.002 |
| 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".