A Proposed Framework of Reference for the Evaluation of Nursing Information Systems
Bibliographic record
Abstract
PURPOSE To develop, implement, and evaluate a clinical knowledgeñbased nursing information system (NIS) to assist nurses working in the field of geriatrics. METHODS Using a model familiar to information systems analysis, the author proposes a model in six dimensions (the quality of the system, the quality of the information, uses for the information, user satisfaction, individual impact, and organizational impact) to measure information system success. A Delphi survey was used to identify indicators to evaluate the success of a NIS. Sixty indicators were submitted to 24 nurses working in different clinical settings from various areas of the province of Quebec. The participants were asked to give their opinion on the pertinence and the clarity of definition of the submitted indicators. To be considered, an indicator had to obtain the group's consensus (85%) regarding its pertinence and the clarity of its wording. FINDINGS The Delphi Group reached consensus on 50 indicators divided amongst the six dimensions. The choice of various indicators to evaluate an NIS will depend on its very nature and on the concerns for assessment. The results of this work led us to propose a framework of reference for the evaluation of nursing information systems. The chosen indicators constitute a set that can be used to evaluate the success of other NISs. CONCLUSIONS Future research is needed to identify the best methods to assess the chosen indicators and clarify the definition of those indicators that have been judged pertinent but are not clearly stated.
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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 | 0.158 | 0.184 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".