Examining the Functionality of the DeLone and McLean Information System Success Model as a Framework for Synthesis in Nursing Information and Communication Technology Research
Bibliographic record
Abstract
In this review, studies examining information and communication technology used by nurses in clinical practice were examined. Overall, a total of 39 studies were assessed spanning a time period from 1995 to 2008. The impacts of the various health information and communication technology evaluated by individual studies were synthesized using the DeLone and McLean's six-dimensional framework for evaluating information systems success (ie, System Quality, Information Quality, Service Quality, Use, User Satisfaction, and Net Benefits). Overall, the majority of researchers reported results related to the overall Net Benefits (positive, negative, and indifferent) of the health information and communication technology used by nurses. Attitudes and user satisfaction with technology were also commonly measured attributes. The current iteration of DeLone and McLean model is effective at synthesizing basic elements of health information and communication technology use by nurses. Regardless, the current model lacks the sociotechnical sensitivity to capture deeper nurse-technology relationalities. Limitations and recommendations are provided for researchers considering using the DeLone and McLean model for evaluating health information and communication technology used by nurses.
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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 | 0.087 | 0.147 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.034 | 0.026 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".