Improving Nasopharyngeal Swab Technique via Simulation for Frontline Workers
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Nasopharyngeal swabs currently remain the gold standard for COVID-19 sample collection. A surge in testing volume has resulted in a large number of health care workers who are unfamiliar with nasal anatomy performing this test, which can lead to improper collection practices culminating in false-negative results and complications. Therefore, we aimed to assess the accuracy and educational potential of a realistic 3D-printed nasal swab simulator to expedite health care workers' skill acquisition. STUDY DESIGN: Prospective pre-post interventional study. METHODS: A nasal swab task trainer (NSTT) was developed to scale from computed tomography data with a deviated septum. Frontline workers at COVID-19 testing sites in Ontario, Canada, were recruited to use the NSTT for nasopharyngeal swab training. Integrated video recording capability allowed participants to self-evaluate procedure accuracy. A five-point Likert scale was collected regarding the NSTT's educational value and procedural fidelity. RESULTS: Sixty-two frontline workers included in the study were primarily registered nurses (52%) or paramedics (16%). Following simulator use, self-assessed accuracy improved in 77% of all participants and 100% of participants who expressed low confidence before training. Ninety-four percent reported that the NSTT provided a complete educational experience, and 82% regarded the system as a more effective training approach than what is currently available. Eighty-one indicated that the simulator should be used at all COVID-19 testing sites, with 77% stating province-wide implementation was warranted. CONCLUSIONS: The nasal swab task trainer is an effective educational tool that appears well-suited for improved skill acquisition in COVID-19 testing and may be useful for training other nasal swab applications. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:38-42, 2023.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".