Factors affecting nurse interns’ compliance with standard precautions for preventing stick injury
Bibliographic record
Abstract
Aim : Compliance with standard precautions (SPs) is a critical workplace safety issue for nurse interns (NIs) as they are clinically incompetent, and obligated to cover nursing shortage in intensive care units (ICUs). Thus current study aimed to assess factors affecting NIs’ compliance with SPs. Methods : Descriptive study design was used. The sample included 110 NIs trained in ICUs at University Hospitals. Tools: Tool (I) Factors affecting NIs’ compliance with SPs included items on compliance with SPs, environmental risk factors, stick injuries, and vaccination, influence of role-modeling and refreshment program on compliance with SPs. Tool (II) Knowledge test covered SPs and transmission of blood-borne pathogens. Results : About 40% of NIs noncompliance with SPs due to lack in supplies and equipments, Majority of them had low and moderate knowledge level regarding SPs. 71.8% had 4-6 times needle stick injuries and 88.2% didn’t report it. 39.1% never use protective equipment in emergency and 29.1% always recap contaminated needles. Conclusions : NIs are at risk of stick injury as they lacking knowledge and skills regarding SPs. Moreover, lacking of supplies and training programs regarding SPs, and absence of reporting system of these incidents contribute to NIs noncompliance with SPs thus they are more at risk of blood transmitted diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".