The Evolution of Medical Student Competencies and Attitudes in Digital Health Between 2016 and 2022: Comparative Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Modern healthcare systems worldwide are facing challenges, and digitalization is viewed as a way to strengthen healthcare globally. As healthcare systems become more digital, it's essential to assess healthcare professionals' competencies and skills to ensure they can adapt to new practices, policies, and workflows effectively. OBJECTIVE: The aim of this study was to analyse how the attitudes, skills and knowledge of medical student concerning digital health have shifted from 2016 to 2022 in connection with the development of the national healthcare information system architecture utilising the Clinical Adoption Meta-Model framework. METHODS: The study population consisted of fifth-year medical students from one University in Finland during 2016, 2021 and 2022. A survey questionnaire was administered comprising seven background questions and 16 statements rated on a five-point Likert scale assessing students' attitudes towards digital health and their self-perceived digital capabilities. The results were recategorized into a dichotomous scale. The statistical analysis employed Pearson's chi-square test. The Benjamini-Hochberg procedure was used for multiple variable correction. RESULTS: The study included 215 medical students (n = 45 in 2016, n = 106 in 2021, and n = 64 in 2022) with an overall response rate of 53% (43% in 2016, 74% in 2021, and 42% in 2022). Throughout 2016, 2021, and 2022, medical students maintained positive attitudes towards using patient-generated information and digital applications in patient care. Their self-perceived knowledge of the national patient portal significantly improved, with agreement increasing by 35 percentage points from 2016 to 2021 (P<.001) and this trend continued in 2022 (P<.001). However, their perceived skills in using electronic medical records did not show significant changes. Additionally, students' perceptions of the impact of digitalization on health promotion improved markedly from 2016 to 2021 (with agreement rising from 53% to 78%, P=.002) but declined notably again by 2022. CONCLUSIONS: Medical students' attitudes and self-perceived competencies have shifted over the years, potentially influenced by the national health information system architecture developments. However, these positive changes have not followed a completely linear trajectory. To address these gaps, educational institutions and policymakers should integrate more digital health topics into medical curricula and provide practical experience with digital technologies to keep professionals up-to-date with the evolving healthcare environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".