Just-in-time training for nurse administration of anesthetic cream application
Bibliographic record
Abstract
Introduction: Just-In-Time Training (JITT) is an emerging concept in medical education. Providing a brief educational intervention in the form of a video immediately prior to the patient intervention may be an effective method to teach health care providers. In this pilot study, we sought to determine if pediatric emergency department nurses could effectively apply a topical anesthetic to the lower back for lumbar puncture after watching a JITT video. Methods: Between October 19, 2011 and November 1, 2011, thirty nurses were asked to complete a questionnaire assessing their comfort level on applying a topical anesthetic prior to lumbar puncture. Accuracy of sham cream placement onto the back of an infant mannequin was assessed pre- and post-JITT video. Self-reported comfort levels in cream placement, cream coverage, and overall accuracy of cream placement were compared both pre- and post-JITT intervention. Results: There was a statistically significant change in the self-reported comfort level of the subjects in applying the cream post-JITT ( p < .01). In addition, there was a statistically significant difference in cream coverage after the JITT ( p < .01). Furthermore, subjects who were inaccurate with cream placement pre-JITT were more likely to be accurate post-JITT ( p < .01). Conclusions: JITT is an effective tool in medical education for teaching topical anesthetic cream application onto the back by pediatric emergency department nurses. JITT may offer other possibilities for enhancing medical education.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".