Smartphone Medical Apps Use by Health Professionals: Is Gender a Confounding Factor?
Bibliographic record
Abstract
The main aim of the study was to establish, whether the gender of a health professional affects the use of smartphone medical apps. We studied the basic patterns of smartphone use in doctors with the largest addiction clinic in Moscow, engaged in in-patient treatment, to access possible gender-determined “digital divide”, evaluate the current use of medical applications, and eventual intentions to use a decision-support app. We performed a cross-sectional study of a non-probability sample of medical doctors using a non-standardized anonymous self-questionnaire, covering 3 domains: socio-demographic and professional characteristics; present use of a mobile device; attitudes to the use of mobile medical apps. The study covered 212 of the 328 staff members, 56% men and 44% women. The largest age group was 41–50 years old (32.1%), followed by 51–60 (25%), 31–40 (23.6%), over 61 (10.8%) and 20-30 (8.5%). 77.8% of respondents use mobile Internet in the office to search for professionally relevant information. 86.5% would like to use mobile applications that help in their professional activities. We failed to confirm the hypothesis about possible gender-related features in the use of mobile devices in doctors. The dedicated mobile system for supporting clinical decision-making in addiction hospitals may be in-demand. The level of doctors’ use of mobile devices and mobile applications shows the absence of gender barriers to the utilization of such systems. In the future, we recommend studying other socio-demographic and occupational predictors affecting the use of professional mobile applications by health professionals of various specialties and the acceptability of the gaming approach in the field.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".