Doctors and the Etiquette of Mobile Device Use in Trauma and Orthopedics
Bibliographic record
Abstract
BACKGROUND: The etiquette surrounding the use of mobile devices, so-called "mobiquette," has been previously identified as a barrier to use in an educational context. OBJECTIVE: To investigate the influence of mobile device use on patient and staff opinions in the trauma and orthopedics department at a teaching hospital in Wales. METHODS: A survey of patients at the bedside and staff in their work environment was undertaken. Data included age, frequency of observed use, suspected main reason for use, and whether doctors' use of a mobile device positively or negatively influenced participants' opinions of them as a professional and as a person. RESULTS: A total of 59 patients and 35 staff responded. The modal age range was 40 to 54 years old. Most patients (78%) never see doctors using mobile devices in the workplace, compared with 3% of staff. The main reason for use was thought to be "communicating with colleagues" (48%) followed by "Internet use/applications for work reasons" (40%). Approximately 40% of patients' opinions of doctors were positively influenced by device use, compared with 82% of staff. This difference between patient and staff opinions was statistically significant for both professional (P<.001) and personal (P=.002) opinions. CONCLUSIONS: Patients are likely to have a negative opinion of doctors using mobile devices in the workplace. This can be balanced by the more positive opinions of colleagues. We advise doctors to remember "mobiquette" around patients.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".