Generational Differences in Hospital Technology Adoption: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The advancement of technological change within healthcare means that it is essential for nurses to have the necessary technological skills to deliver safe and efficient nursing care. Few studies have examined whether generational differences affect the adoption of technology within the healthcare system. AIM: The primary purpose of this study was to explore predictors that influence the adoption of technology. METHODS: In this cross-sectional study, nurses were asked to rate their level of competency on 20 key skills related to clinical technological devices (CTDs) in a self-administered questionnaire. Participants' demographic data and level of proficiency related to personal computer skills were also collected. Multiple linear regression analysis was used to examine whether demographic characteristics and personal computer skills predicted higher scores related to CTDs. RESULTS: Sixty-three nurses completed the questionnaires. Overall mean score for skills related to CTD was high at 3.74 (SD = 0.75) out of 5. Length of employment at the hospital and previous exposure to the technology used at the hospital (β = 0.06, p = 0.021; β = 0.054, p = 0.011, respectively) were the only variables significantly associated with higher CTD skills scores. Generational cohort, gender, years of nursing experience and self-rated proficiency related to personal computer skills were not related to higher CTD skills scores. CONCLUSION: The results of this study emphasize that consistent exposure to technology enhances its adoption. Generational cohort did not play a role in the perception of nurses' technology competency at Humber River Hospital.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".