Exploring Generational Differences in Physicians’ Perspectives on the Proliferation of Technology within the Medical Field: A Narrative Study
Bibliographic record
Abstract
BACKGROUND: The development and advancement of information and communication technologies (ICTs), such as electronic libraries, electronic medical records and computerized physician order entry systems, have made learning and acquiring vast medical knowledge feasible. However, there are limited data pertaining to the navigation of such technologies among physicians of varying generational cohorts. OBJECTIVE: The aim of this study was to explore physician experiences and perspectives influencing the adoption of ICTs, with an emphasis on generational differences. METHODS: Semi-structured interviews with focus groups or individual physicians were conducted, recorded and transcribed to elicit key themes. RESULTS: Across the generations, participants expressed several benefits to ICTs, such as accessibility, efficiency and use of current, evidence-based practice medicine. Common problems encountered included usability issues, downtimes, alarm fatigue, and administrative tasks. There were differences between generations regarding adaptability, perceived benefits and drawbacks and perceptions of other generations' ability to adapt. CONCLUSION: Physicians from various generations recognized the overall benefits of implementing ICTs. Although some drawbacks were reported, all participants understood the necessity of ICTs. Furthermore, implementation should be tailored to physician working style and learning needs.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".