Evidence in the Palm of Your Hand: Development of an Outcomes‐Focused Knowledge Translation Intervention
Bibliographic record
Abstract
AIM: The aim of the project was to develop an electronic information gathering and dissemination system to support both nursing-sensitive outcomes data collection and evidence-based decision-making at the point-of-patient care. BACKGROUND: With the current explosion of health-related knowledge, it is a challenge for nurses to regularly access information that is most current. The Internet provides timely access to health information, however, nurses do not readily use the Internet to access practice information because of being task-driven and coping with heavy workloads. Mobile computing technology addresses this reality by providing the opportunity for nurses to access relevant information at the time of nurse-patient contact. METHOD: A cross-sectional, mixed-method design was used to describe nurses' requirements for point-of-care information collection and utilization. The sample consisted of 51 nurses from hospital and home care settings. Data collection involved work sampling and focus group interviews. FINDINGS: In the hospital sector, 40% of written information was recorded onto "personal papers" at point-of-care and later transcribed into the clinical record. Nurses often sought information away from the point-of-care; for example, centrally located health records, or policy and procedure manuals. In home care, documentation took place in clients' homes. The most frequent source of information was "nurse colleagues." Nurses' top priorities for information were vital signs data, information on intravenous (IV) drug compatibility, drug references, and manuals of policies and procedures. IMPLICATIONS: A prototype software system was designed that enables nurses to use handheld computers to simultaneously document patients' responses to treatment, obtain real-time feedback about patient outcomes, and access electronic resources to support clinical decision-making. CONCLUSION: The prototype software system has the potential to increase nurses' access to patient outcomes information and evidence for point-of-care decision-making.
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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 | 0.060 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".