Usage of smart devices amongst medical practitioners in Universitas Academic Hospital
Bibliographic record
Abstract
BACKGROUND: There has been a rapid rise in the use of smart devices amongst medical practitioners throughout the world. This study aimed to identify how smart devices were being used by medical practitioners at the Universitas Academic Hospital (UAH), Bloemfontein, and the associated factors thereof. We also identified the views of medical practitioners regarding the usage of smart devices at their workplace. METHODS: A prospective cross-sectional study was conducted. Anonymous questionnaires were distributed to medical practitioners working at UAH during weekly departmental meetings or monthly morbidity and mortality meetings. The following largest departments were included: Surgery, Anaesthetics, Paediatrics, Internal Medicine, Family Medicine, and Obstetrics and Gynaecology. RESULTS: The response rate was 82.7% of those attending the meetings. All the respondents owned a smart device and brought it to their workplace. The most common applications used on these smart devices were that for drug references (65.9%), medical textbooks (63.6%) and medical calculators (58.1%). Significantly larger percentages of doctors aged 21-39 years compared with those aged 40-65 years used drug reference applications and medical calculators. A quarter (24.8%) of respondents communicated with patients through a smart device, 21.7% used an online storage platform to backup patient data, whilst 56.6% used their devices to store and view patient information. More than one-third (36.7%) agreed that smart devices threatened patient confidentiality, but the majority (58.8%) did not agree that these devices hinder patient communication. The majority felt that these devices improved both personal performance (69.2%) and patient care (79.0%). CONCLUSION: Smart devices usage is common in this setting. Hence, integration of such usage in medical curricula, discussion on professionalism, ethics and confidentiality in this context, and guidance from institutions and professional bodies become necessary.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".