Prevalence, pattern, and associated psychosocial factors of headache among undergraduate students of health profession
Bibliographic record
Abstract
Background Headache is a common health problem among health professional students which may negatively affect their academic performance and quality of life. The study aimed to determine the prevalence, pattern, associated psychosocial factors and treatment seeking behavior regarding headache among the students of health profession. Methods The cross sectional study was undertaken during April–September 2016 involving 339 students studying medical, dental and nursing health sciences in KIIT University, Bhubaneswar, India. A semi-structured questionnaire was used to collect all relevant information. Numeric Pain Rating Scale (NPRS) and Headache Attributed Lost Time (HALT) index were used to assess subjective perception regarding intensity of pain over the previous 24 h and disability burden over the last 3 months respectively. Results The one year prevalence of headache was 73.1%, of which migraine and tension type headache were 33.3% and 19.2% respectively. In majority (93.2%) of the students, the intensity of pain was mild to moderate. More than one third (37.1%) of students reported that headache was affecting their family, social and leisure activities with moderate-severe impact indicating high need of medical care. Logistic regression analysis revealed that female gender (adjusted OR: 2.67), problem in falling asleep (adjusted OR: 2.86), problem in staying asleep (adjusted OR: 11.11), soft drink consumption (adjusted OR: 2.22) and self dissatisfaction with own health (adjusted OR: 1.89) were significantly associated with headache. Conclusion High prevalence of headache among the students of health profession necessitates designing of appropriate strategies to improve the quality of life in this population.
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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".