The use of smartphones on General Internal Medicine wards
Bibliographic record
Abstract
OBJECTIVE: To describe the uses of institutional and personal smartphones on General Internal Medicine wards and highlight potential consequences from their use. METHODS: A mixed methods study consisting of both quantitative and qualitative research methods was conducted in General Internal Medicine wards across four academic teaching hospitals in Toronto, Ontario. Participants included medical students, residents, attending physicians and allied health professionals. Data collection consisted of work shadowing observations, semi-structured interviews and surveys. RESULTS: Personal smartphones were used for both clinical communication and non-work-related activities. Clinicians used their personal devices to communicate with their medical teams and with other medical specialties and healthcare professionals. Participants understood the risks associated with communicating confidential health information via their personal smartphones, but appear to favor efficiency over privacy issues. From survey responses, 9 of 23 residents (39%) reported using their personal cell phones to email or text patient information that may have contained patient identifiers. Although some residents were observed using their personal smartphones for non-work-related activities, personal use was infrequent and most residents did not engage in this activity. CONCLUSION: Clinicians are using personal smartphones for work-related purposes on the wards. With the increasing popularity of smartphone devices, it is anticipated that an increasing number of clinicians will use their personal smartphones for clinical work. This trend poses risks to the secure transfer of confidential personal health information and may lead to increased distractions for clinicians.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".