Predictors of Nurses??? Acceptance of an Intravenous Catheter Safety Device
Bibliographic record
Abstract
BACKGROUND: It is important to determine the factors that predict whether nurses accept and use a new intravenous (IV) safety device because there are approximately 800,000 needlesticks per year with the risk of contracting a life-threatening bloodborne disease such as HIV or hepatitis C. OBJECTIVES: To determine the predictors of nurses' acceptance of the Protectiv Plus IV catheter safety needle device at a teaching hospital in Texas. METHOD: A one-time cross-sectional survey of nurses (N = 742) was conducted using a 34-item questionnaire. A framework was developed identifying organizational and individual predictors of acceptance. The three principal dimensions of acceptance were (a) satisfaction with the device, (b) extent to which the device is always used, and (c) nurse recommendations over other safety devices. Measurements included developing summary subscales for the variables of safety climate and acceptance. Descriptive statistics and multiple linear and logistic regression models were computed. RESULTS: The findings showed widespread acceptance of the device. Nurses who had adequate training and a positive institutional safety climate were more accepting (p <or=.001). Also, nurses who worked at the hospital a shorter period were more likely to be accepting of the device (p <or=.001). Nurses who felt that the safety climate was positive and who had used the device for at least 6 months were more likely to use the device (p
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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 | 0.018 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".