Technology readiness of medical students and the association of technology readiness with specialty interest
Bibliographic record
Abstract
Background: Technology readiness (TR) is a construct which characterizes an individual’s propensity to utilize new technology. Despite increased technology use in healthcare, limited data exists on medical student TR and the relation of TR with specialty interest. This study assesses the TR of 2nd year medical students and its association with specialty interest. Methods: Respondents completed a survey assessing their most preferred specialty, specialty interests, and technology readiness using a 5-point Likert scale. Using Chi-square analysis, we examined the relation between demographics, TR, and specialty interest. Results: This study obtained a 45.7% (n = 53/116) response rate demonstrating that 79.2% (n = 42/53) of students were “technology ready.” Male students were more likely to be technology ready (95.2%, n = 20/21, vs 68.8%, n = 22/32, p = 0.02) when compared to female students. Technology ready students were associated with being more interested in “Technology-Focused” specialties compared to students who were not technology ready (88.5%, n = 23/26 vs 70.4%, n = 19/27, p = 0.104). Conclusions: As a cohort, most medical students were technology ready. It is inconclusive if technology ready students are more likely to be interested in technology-focused specialties due to the limited sample size of this study, although with an increased sample size, an improved understanding on technology readiness and its potential impact on student specialty interest may be obtained. Furthermore, knowledge of TR may aid in developing targeted technology-based education programs and in improving remedial approaches for students who are less comfortable with new technology.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".