Home Disposal of Used Insulin Syringes and Needles by the Patients With Diabetic in Rwanda
Bibliographic record
Abstract
For many people living with diabetes, using needles to inject insulin and test blood glucose levels is a part of their everyday lives. Improper disposal of these highly contaminating sharp materials can cause injuries to people as well as pollute water sources and agricultural land.Approximately 3.4% of the population in Rwanda lives with diabetes. There is no law specifying how these individuals should dispose of home-used syringes and lancets, and prior to this study, home disposal practices for sharp instruments including needles and lancets used for diabetes self-management were unknown.A cross-sectional study design was used to identify the common methods used by people living with diabetes to dispose of sharps after home use. A total of 201 people living with diabetes participated in the study. Only 107 (53.3%) of them could identify the proper methods of sharp disposal and only 69 (34.3%) reported using these proper disposal methods. The top three challenges to the use of proper disposal practices reported by participants included not being informed of such practices (76, 37.8%), not having appropriate containers (66, 32.8%) and having to travel a long distance to return safety boxes containing sharp materials (47, 23.4%).Future studies should be conducted to understand the financial feasibility of health facility provision of safe disposal boxes for patients. Education focusing on training people living with diabetes to use hard plastic bottles as an acceptable alternative to boxes is also needed. Convenient and effective mechanisms for obtaining and returning used safety boxes should be established. Larger scale studies including more patients could generate more representative data.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".