Drug Shortages and Its Effects on Service Delivery among Key Informants (KIs) in Fiji
Bibliographic record
Abstract
BACKGROUND: Drug shortage is a global problem that adds extra burden to health systems with additional costs and posing risks to patients who fail to receive the medicines they need. Aim This study aimed to describe drug shortage and its effects on service delivery among Key Informants (KIs) in Fiji. MATERIALS AND METHODS: In this quantitative cross-sectional study, the information on drug shortage per month was collected through the stock status report and pharmacy records of the main referral hospital in Fiji, the Colonial War Memorial hospital (CWMH) from 1st June to 30th November 2015. Purposive sampling was applied to reach 50 KIs who met the study crietria. A pilot tested structured questionniare was used to collect data and descriptive statistic was used to analyze data. RESULTS: The results showed the most frequent out of stock items were antibiotics (49) followed by vitamins (18) and antihypertensive (17). The frequency of total drugs on shortage ranged from 29 to 50 items per month with an average rate 10% (n=40) drug shortages per month. Additinally, 98% (n=49) of the KIs indicated that drug shortage increased workload of staff and 90% (n=45) indicated that the standard treatment was deviated or alternatives were sought due to shortage of drugs. CONCLUSIONS: The findings of this study revealed that drug shortage was experienced in CWMH during the study period and had an impact on the clinical service delivery. There is a needed to focus on the use, selection, procurement and storage and distribution of medicines at CWMH.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".