Uncovering the Decision-Making Process of Wound Management by Nurses
Bibliographic record
Abstract
Background: While recognition of the complexities and importance of wound management continues to grow globally, research has only recently begun exploring how clinicians form decisions related to the management of wounds. Improving recognition of the decision-making process may have profound implications on how clinicians are educated and supported in their approach to managing wounds. Purpose: To identify the basic social process underlying the naturalistic decision-making process in wound management for nurses in acute care settings. Methods: A scoping review on naturalistic decision making in nursing was initiated to begin this two-phase study. The second phase was a study utilizing a multi-grounded theory design combined with the use of a Naturalistic Decision-Making (NDM) Framework and the Model of Recognition-primed Decision Making. Ten participants were included using maximum variation and theoretical sampling methods. Participants included acute care Nurses Specialized in Wound, Ostomy and Continence across Canada, as well as, Registered Nurses, and Registered Practical Nurses from a regional acute care facility in Ontario, Canada. Interviews with participants occurred, in an identical fashion, either in person or via electronic means using Zoom or Microsoft Teams. Results: The results demonstrate the process taken by nurses when making a decision considering prior experience. Ten themes were identified in which the nurse seeks out new experience or draws upon experience through recognition of education, patient goals, expected outcomes, relevant cues, actions, critical thinking, and if necessary, modification. This process is followed by reassessment and reflection on practice. Conclusion: The development of the nurse wound care decision-making process provides greater insight into the decision-making process undertaken by nurses when caring for a patient with a wound. The findings of this study will assist administrators, policy developers, and those providing nursing education with insight into the decision-making process. These insights will assist in developing techniques to support and influence appropriate evidence-based decision-making in 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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".