Nurses' perceptions and attitudes towards new ADU technology and use
Bibliographic record
Abstract
BACKGROUND: The introduction of a new technology in hospitals – Automated Dispensing Units (ADUs) –aims to contribute to more secure, safe, efficient and cost effective health services. Several studies highlight the beneficial effects of similar technologies as well as their cost-savings potential but there is little literature exploring nurses’ perceptions and attitudes towards technology acceptance and the impact on technology use in a healthcare unit. OBJECTIVE: This research aimed to explore nurses’ perceptions and attitudes towards current technology use on their units and towards the introduction of ADU technology and use with nursing staff in two different hospitals in South-East New-Brunswick, Canada. METHODS: Semi-structured interviews were realized with the collaboration of nursing staff from two hospitals which were in urban and rural settings, prior to the introduction of ADUs in hospital wards. RESULTS: Findings in this study highlight the fact that missing medications (i.e., doses not available in cart) are inherently related to the completion of nursing staff’s medication distribution routine. Missing doses cause delays in medication delivery which may increase the occurrence of medication errors. Participants described current technology use as an intricate part of their routine. The latter is mainly utilized for patient monitoring and information retrieval. Overall, interview data indicated that ADU technology introduction is positively perceived by nursing staff particularly if the technology reduces missing doses events. CONCLUSIONS: Findings in this study underscore important concerns expressed by nursing staff regarding ADU technology integration into the current medication process and its impact on time management. Pre-implementation training and technical support were identified as important factors in facilitating technology acceptance and proper technology use.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".