Supporting Evidence-Based Practice for Nurses through Information Technologies
Bibliographic record
Abstract
PURPOSE: To evaluate the usability of mobile information terminals, such as personal digital assistants (PDAs) or Tablet personal computers, to improve access to information resources for nurses and to explore the relationship between PDA or Tablet-supported information resources and outcomes. BACKGROUND: The authors evaluated an initiative of the Nursing Secretariat, Ontario Ministry of Health and Long-Term Care, which provided nurses with PDAs and Tablet PCs, to enable Internet access to information resources. Nurses had access to drug and medical reference information, best practice guidelines (BPGs), and to abstracts of recent research studies. METHOD: The authors took place over a 12-month period. Diffusion of Innovation theory and the Promoting Action on Research Implementation in Health Services (PARIHS) model guided the selection of variables for study. A longitudinal design involving questionnaires was used to evaluate the impact of the mobile technologies on barriers to research utilization, perceived quality of care, and on nurses' job satisfaction. The setting was 29 acute care, long-term care, home care, and correctional organizations in Ontario, Canada. The sample consisted of 488 frontline-nurses. RESULTS: Nurses most frequently consulted drug and medical reference information, Google, and Nursing PLUS. Overall, nurses were most satisfied with the Registered Nurses Association of Ontario (RNAO) BPGs and rated the RNAO BPGs as the easiest resource to use. Among the PDA and Tablet users, there was a significant improvement in research awareness/values, and in communication of research. There was also, for the PDA users only, a significant improvement over time in perceived quality of care and job satisfaction, but primarily in long-term care settings. IMPLICATIONS: It is feasible to provide nurses with access to evidence-based practice resources via mobile information technologies to reduce the barriers to research utilization.
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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 | 0.197 | 0.493 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".