Vaping: Impact of Improving Screening Questioning in Adolescent Population: A Quality Improvement Initiative
Bibliographic record
Abstract
The use of Electronic Nicotine Delivery Systems (ENDS) increased dramatically over the past decade, making them the most common tobacco product used among youth. While physicians often screen for the use of tobacco, very few screen for vaping product usage. This quality improvement project aimed to increase the screening rate of ENDS use among adolescents to 85% to match the Healthy People 2020 screening target of 83.3% for smoking. METHOD: We collected data from weekly chart reviews of all adolescent visits with a primary care provider by using keywords such as "vapor," "e-cigs," and "vaping" to document screening for ENDS use. The project consisted of 4 PDSA cycles: (1) education of the Adolescent Clinic staff about screening; (2) the addition of the specific question for e-cigarette use in the facility's Electronic Health Record; (3) house staff lecture about the importance of screening; and (4) reinforcement about screening to adolescent physicians. RESULTS: The percentage of screening for traditional tobacco use was consistently higher than ENDS use in all months. ENDS use assessment increased since the first intervention, going from 0% at baseline to 90% at the end. The addition of a specific question for ENDS use in EPIC was the most significant intervention and increased the screening percentage to 78%. CONCLUSIONS: To adequately assess for the use of ENDS, the nonspecific question, "do you smoke?" is not sufficient. A direct approach is necessary. A specific question in the EHR is the most significant way to increase screening for ENDS use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".