Design and development of the BD Odon Device <sup>TM</sup> : a human factors evaluation process
Bibliographic record
Abstract
OBJECTIVE: To (1) determine how intended users interact with and use the BD Odon Device in simulation, (2) use these findings to alter progressively the design of the BD Odon Device and (3) validate that these changes have improved the ability of practitioners to use the BD Odon Device. DESIGN: Human factors evaluation study. SETTING: Simulation suite designed to mimic delivery room. POPULATION OR SAMPLE: Three hundred and ninety simulated operative births, performed by 100 practising clinicians. METHODS: Simulated operative vaginal births performed using the BD Odon Device and the device Instructions for use were subjected to three formative human factors evaluations and one human factors validation test. Following each evaluation, findings were reviewed and the design of the BD Odon Device and Instructions for use were modified. MAIN OUTCOME MEASURES: Successful performance of an operative vaginal birth using the BD Odon Device in accordance with provided training and Instructions for use. RESULTS: Using version two of the BD Odon Device, and following exposure to face-to-face training and written instructions, 25% of accouchers were able successfully to perform a simulated operative vaginal birth. In the final evaluation, following device design and training material alterations, all accouchers were able successfully to perform a simulated operative vaginal birth using version four of the BD Odon Device. CONCLUSIONS: Human factors evaluations have enabled a multi-professional device and training materials design team to alter the design of the BD Odon Device and the Instructions for use in an evidence-based fashion. This process has resulted in a device which has a predictable and likely safe pattern of use. TWEETABLE ABSTRACT: Human Factors evaluations help make the BD Odon Device safe and usable for clinical practice.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".