Bibliographic record
Abstract
Objective: The study is aimed at evaluating the reasons of medication administration errors occurrence and not reporting in Pakistan in the light of nurses' regular drug administration process.Methodology: Cross Sectional review based investigation was done in Bolan Medical Complex Hospital Quetta, Pakistan.The pre-consent was taken from all the nurses who agreed to participate in research.The total number of 200 questioners were distributed to the nursing staff of the Bolan Medical Complex Hospital Quetta, Pakistan.The 180 questioners were returned out of which 7 questioners were excluded from the examination because of the inadequacy.The remaining 168 questioners were considered in the examination.All data were gathered, coded, classified and factual examination is performed by SPSS 20.Result: For the reasons, why medication administration errors occur, most of the nurses (133=79.2%)consented to the statement, "There is no relaxed method to look up evidence on medicines".For the reasons, why medication administration not reporting, most of the nurses (154=91.7%) agreed to the statement, "The patients believe that medicines are given accurately as per instruction".Conclusion: The reasons why medication administration errors occur, include: hard to pick up data taking drugs blunders, messy medicine request, and Pharmacist not being accessible for 24 hours.The factors clarifying why staff nurses may not report medicine mistakes, include: uplifting desire from nurses, blunder definition reasons, and fear from the patient family, doctor and nursing administration.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.836 | 0.847 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".