Defining patient deterioration through acute care and intensive care nurses' perspectives
Bibliographic record
Abstract
AIM: To explore the variations between acute care and intensive care nurses' understanding of patient deterioration according to their use of this term in published literature. BACKGROUND: Evidence suggests that nurses on wards do not always recognize and act upon patient deterioration appropriately. Even if resources exist to call for intensive care nurses' help, acute care nurses use them infrequently and the problem of unattended patient deterioration remains. DESIGN: Dimensional analysis was used as a framework to analyze papers retrieved in a nursing-focused database. METHOD: A thematic analysis of 34 papers (2002-2012) depicting acute care and intensive care unit nurses' perspectives on patient deterioration was conducted. FINDINGS: No explicit definition of patient deterioration was retrieved in the papers. There are variations between acute care and intensive care unit nurses' accounts of this concept, particularly regarding the validity of patient deterioration indicators. Contextual factors, processes and consequences are also explored. CONCLUSIONS: From the perspectives of acute care and intensive care nurses, patient deterioration can be defined as an evolving, predictable and symptomatic process of worsening physiology towards critical illness. Contextual factors relating to acute care units (ACU) appear as barriers to optimal care of the deteriorating patient. This work can be considered as a first effort in modelling the concept of patient deterioration, which could be specific to ACU. RELEVANCE TO CLINICAL PRACTICE: The findings suggest that it might be relevant to include subjective indicators of patient deterioration in track and trigger systems and educational efforts. Contextual factors impacting care for the deteriorating patient could be addressed in further attempts to deal with this issue.
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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 | 0.039 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".