Local anesthesia before intravenous cannula insertion: Recommendations for registered nurses in practice
Bibliographic record
Abstract
Objective: The purposes of this project were to educate registered nurses of the intradermal, pretreatment procedure; provide education on the hospital’s current IV therapy pretreatment policy; and increase the usage of intradermal, local anesthesia for cannulation for adult patients’ comfort level.Methods: A mixed method of nonexperimental descriptive pre- and post-survey was used. The data was collected from 48 registered nurses’ pre- and post-surveys indicating descriptive analysis. The descriptive analysis identified barriers as to why registered nurses were not using pretreatment prior to IV insertion. Results: The results revealed the majority of the participants (83%) were not aware of the hospital’s IV pretreatment policy of intradermal anesthesia with Lidocaine before IV insertion prior to the DNP project. Evidence indicated inconsistency in the use of pain management strategies during these procedures. The conclusions of this project provided an important overview of the barriers to change in clinical practice for registered nurses with IV skills.Conclusions: An improvement project educational program, such as an educational video on how to preform intradermal pretreatment to an IV site prior to IV insertion and utilization of a manikin IV arm for simulation practice, was developed and recommended to a nursing IV therapy practice for registered nurses. Local anesthetic, such as intradermal, should become standard practice for registered nurses regarding pretreatment for pain control prior to intravenous insertion.
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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 | 0.038 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".