Exploring the technology readiness of nursing and medical students at a Canadian University
Bibliographic record
Abstract
Technology readiness is a well-established construct that refers to individuals' ability to embrace and adopt new technology. Given the increasing use of advanced technologies in the delivery of health care, this study uses the Technology Readiness Index (Parasuraman, 2000) to explore the technology readiness of nursing and medical students from the fall 2006 cohort at Memorial University of Newfoundland. The three major findings from this study are that (i) rural nursing students are more insecure with technology than their urban counterparts, (ii) male medical students score higher on innovation than their female counterparts and have a higher overall technology readiness attitude than female medical students, and (iii) medical students who are older than 25 have a negative technology readiness score whereas those under 25 had a positive score. These findings suggest health care professional schools would be well served to implement curricular changes designed to support the needs of rural students, women, and those entering school at a non-traditional age. In addition, patterns such as those observed in this study highlight areas of emphasis for current practitioners as health care organizations develop continuing education offerings for staff.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".