How to Harness the Power of the Possible – Mobile Point of Care Information
Bibliographic record
Abstract
Mobile technology and hand-held communication devices are now a significant part of life for many Canadians. The data is clear; nurses and nursing students have access to appropriate technology like never before. The profession is harnessing the power of technology to access and improve care and knowledge. A recent study by Doran et al (2010) added to the knowledge base found that “It is feasible to provide nurses with access to evidence –based practice resources via mobile information technologies to reduce the barriers to research utilization”. The question is where to start? This article is aimed at assisting the novice mobile user, or the expert social network user to harness the possible power of the technology to improve patient care at the point of care. Concrete examples of applications will be provided to illustrate what is possible, acknowledging that new applications and platforms are emerging daily. Specific attention is provided to student centered applications as well as primary health care applications for practice based nursing professionals, focusing on advanced practice. The concept of harnessing the power of point of care information is not only exciting, but essential in this digital age. It is urgent that we understand how to adopt the use of these devices and to understand the barriers that we face. The power of what is possible with mobile technologies can be staggering; however taking the first step can be far less daunting. Normal 0 21 false false false FR-CA X-NONE X-NONE
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.018 |
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".