The Use of Mobile Phone and Medical Apps among General Practitioners in Hangzhou City, Eastern China
Bibliographic record
Abstract
BACKGROUND: Mobile phones and mobile phone apps have expanded new forms of health professionals' work. There are many studies on the use of mobile phone apps for different specialists. However, there are no studies on the current use of mobile phone apps among general practitioners (GPs). OBJECTIVE: The objective of the study was to investigate the extent to which GPs own smartphones with apps and use them to aid their clinical activities. METHODS: A questionnaire survey of GPs was undertaken in Hangzhou, Eastern China. Data probing GPs' current use of medical apps in their clinical activities and factors influencing app use were collected and analyzed RESULTS: 125 GPs participated in the survey. 90.4% of GPs owned a mobile phone, with 48.7% owning an iPhone and 47.8% owning an Android phone. Most mobile phone owners had 1-3 medical-related apps, with very few owning more than 4. There was no difference in number of apps between iPhone and Android owners (χ(2)=1.388, P=0.846). 36% of GPs reported using medical-related apps on a daily basis. The majority of doctors reported using apps to aid clinical activities less than 30 minutes per day. CONCLUSIONS: A high level of mobile phone ownership and usage among GPs was found in this study, but few people chose medical-related apps to support their clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".