How Do Information and Communication Technologies Influence Nursing Care?
Bibliographic record
Abstract
Despite the well-known advantages of information and communication technologies (ICTs), their overall impact on nursing care has not been synthesized. The objective of this overview of systematic reviews is to summarize the best evidence regarding the effects of ICTs on nursing care. We considered quantitative, qualitative and mixed-method reviews published since January 1995. Two reviewers independently screened the title and abstract of 5515 papers to assess their eligibility. From these, 72 full-text papers were evaluated and 28 publications met the inclusion criteria. Three reviewers extracted and compared their data. Preliminary results show that the following dimensions of nursing care are the most frequently reported: assessment, care planning and evaluation, documentation time, quality of care and patient safety. This overview provides a starting point from which to compare and contrast findings of separate reviews regarding the positive, neutral and negative effects of ICTs on nursing care.
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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 | 0.063 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".