Evaluation of a system for providing information resources to nurses
Bibliographic record
Abstract
The authors describe a study to plan and implement an information system for nurses. The objectives were to (1) determine the clinical information needs of nurses; (2) adapt an existing clinical information system (CLINT) to address their expressed needs; and (3) evaluate nurses’ use of and satisfaction with the enhanced system. Thirty-nine nurses on a medical teaching unit in a tertiary hospital in Canada participated in the project. A needs assessment influenced the design of the nursing interface to CLINT and the development of educational and participatory strategies to promote its use. Data were collected before, after, and throughout the implementation period. Qualitative and quantitative methods, including focus groups, online questionnaires, and automated usage data collection, were used to describe nurses’ use of and satisfaction with the system. The results suggested that peer mentorship, organizational support, and collaboration were the most effective strategies for promoting system use. The hospital information system (IHIS), Netscape, drug information and basic texts were the most frequently used databases. Nurses were satisfied with the system and reported progress in changing clinical practice. CLINT helped them to keep up with educational and professional development. In conclusions, nurses are willing to use information systems that are relevant to their needs and user friendly. There is, however, a paucity of resources available for evidence-based clinical decision making.
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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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".