The emerging role of PDAs in information use and clinical decision making
Bibliographic record
Abstract
One of the great challenges facing healthcare professionals today is the effective and efficient management of an ever-increasing amount of clinically related health information. An important dimension of this challenge is the accessibility of information at times of decision making. Mobile information terminals, such as personal digital assistants (PDAs), have the potential to address this challenge by bringing the most relevant information directly to the point of care. Providing information through convenient electronic sources may address some of the barriers that inhibit access and clinical use of new and relevant research by nurses. The purpose of this Notebook is to explore the use of PDAs to increase nurses’ access to and use of evidence-based resources in practice. It will explore how information and communication technologies, such as PDAs, can support evidence-based practice and will examine the role of information and communication technologies within the context of established knowledge-translation approaches. Recognising that information technologies alone will not change evidence-based practice, the limitations of current technologies will be discussed, drawing on research evidence to argue the importance of considering technological innovation within the context of other knowledge-translation strategies. New or enhanced competencies that will be needed to ensure quality health care were outlined in the publication Crossing the quality chasm .1 They included expertise in evidence-based practice, quality improvement, informatics, and patient-centred care. Each of the skills identified represents a key component of evidence-informed decision making, and they all come together where nurses and patients meet—at the point of care. Nurses must be engaged in continuous learning to acquire patient-centred and treatment-focused information in new and more rewarding ways. Our team has been studying the effectiveness of PDAs and mobile tablet personal computers (tablet PCs) for improving nurses’ access to evidence-based resources at the point of care. Point of care in …
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".