Prevalence and Associated Factors of Regular Nonsteroidal Anti-inflammatory Drugs used in a Rural Community, Thailand
Bibliographic record
Abstract
BACKGROUND: In Thailand, 67.2% of the population widely uses analgesics including nonsteroidal anti-inflammatory drugs (NSAIDs), which may lead to serious side effects. However, the information of regular NSAIDs used in Thailand is still limited.METHODS: A mixed method cross-sectional study was conducted. Quantitative data were collected using questionnaires to determine the prevalence and factors associated with regular NSAID use. The qualitative study was conducted using group and in-depth interviews to determine the knowledge, attitudes and practices of NSAID users.RESULTS: Of 771 participants, the prevalence of NSAID use was 31.1 and regular NSAID use was 7.4. Age, pain at the hips or thighs and pain score were independent factors associated with regular NSAID use. The qualitative study indicated that the use of NSAIDs was influenced by drug effectiveness, sources of NSAIDs and consideration of benefits and risks of the drugs.CONCLUSION: This was the first report on the prevalence and associated factors of regular NSAID use in Thailand. In this community, nonprescribed NSAIDs might cause some serious side effects and undesirable drug interaction. Information on side effects of pain medications should be disseminated to the public including guidelines on how to use pain medications.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".