Pattern of Medicine Prescribing in PHC Facilities before and after earthquake in Nepal
Bibliographic record
Abstract
Introduction: On April and May 2015, Nepal experienced two earthquakes. Many studies have focused on acute care delivery, disease outbreaks, mental health issues, and disaster relief post-earthquakes. Few others have looked at psychiatric medication prescription and health aid distribution pattern, only one study has addressed the effects of an earthquake on medication prescribing patterns and compared them to the post earthquake setting. This paper aims to examine common health problems and prescribing practices before and after the earthquake. Methods: This descriptive retrospective study was conducted within seven randomly selected health posts (HPs) located in the three most earthquake-affected districts of Bhaktapur, Kathmandu and Dhading. The patient records per month from each HP were selected from the out patient department (OPD) register by systematic random sampling for three months prior and three months after the earthquake. There were 584 and 654 encounters in the pre and post earthquake period respectively. Each patient record was analysed using WHO drug use indicators and national treatment guidelines. Results: A significant decrease in encounters receiving antibiotics and cases receiving albendazole alone in worm infestation was found in the post-earthquake period. A significant increase in prescribing antibiotics in cases of common cold was found. Conclusions: The common health problems were similar in both periods. However, prescribing practices were changed. As prescriptions related to mental health problems were lacking, there is a need for improving mental health education to the health workers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".