Bibliographic record
Abstract
Usability evaluations are necessary to determine the feasibility of nurses' interactions with computerized clinical decision-support systems. Limitations and challenges of operations that inhibit or facilitate utilization in clinical practice can be identified. This study provided nurses with mobile information terminals, PDAs and tablet PCs, to improve point-of-care access to information. The purpose of this study was to determine usability issues associated with point-of-care technology. Eleven nurses were self-selected. Nurses were videotaped and audiotaped completing four tasks, including setting up the device and three resource search exercises. A research team member completed a usability checklist. Completion times for each task, success rate, and challenges experienced were documented. Four participants completed all tasks, with an average time of 3 minutes 22 seconds. Three participants were unable to complete any of the three tasks. Navigating within resources caused the greatest occurrence of deviations with 39 issues among all participants. Results of the usability evaluation suggest that nurses require a device that (1) is manageable to navigate and (2) utilizes a user-friendly interface, such as a one-time log-in system. Usability testing can be helpful to organizations as they document issues to be cognizant of during the implementation process, increasing the potential for successful implementation and sustained usability.
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.
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 | 0.035 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".