Exploring the technology readiness of nursing and medical students at a Canadian University
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it