Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To improve performance of a neonatal transport team by implementing a research-based family assessment instrument. Objectives included providing a structure for evaluating families and fostering the healthcare relationship. BACKGROUND/RATIONALE: Neonatal transports are associated with family crises. Transport teams require a comprehensive framework to accurately assess family responses to adversity and tools to guide their practice toward parental mastery of the event. Currently, there are no assessment tools that merge family nursing expertise with neonatal transport. DESCRIPTION OF THE PROJECT: A family assessment tool grounded in contemporary family nursing theory and research was developed by a clinical nurse specialist. The Ottawa Model of Research Use guided the process of piloting the innovation with members of a transport team. Focus groups, interviews, and surveys were conducted to create profiles of barriers and facilitators to research use by team members. Tailored research transfer strategies were enacted based on the profile results. OUTCOME: Formative evaluations demonstrated improvements in team members' perceptions of their knowledge, family centeredness, and ability to assess and intervene with families. The family assessment tool is currently being incorporated into Clinical Practice Guidelines for Transport and thus will be considered standard care. CONCLUSION: Use of a family assessment tool is an effective way of appraising families and addressing suffering. The Ottawa Model of Research Use provided a framework for implementing the clinical innovation. IMPLICATIONS FOR NURSING PRACTICE: A key role of the clinical nurse specialist is to influence nursing practice by fostering research use by practitioners. When developing and implementing a clinical innovation, input from end users and consumers is pivotal. Incorporating the innovation into a practice guideline provides a structure to imbed research evidence into practice.
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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 | 0.088 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".