Nurses' evaluations of the <scp>CPOT</scp> use at 12‐month post‐implementation in the intensive care unit
Bibliographic record
Abstract
BACKGROUND: Performing routine pain assessments with all intensive care unit (ICU) patients is strongly recommended in clinical practice guidelines. As many ICU patients are unable to self-report, the Critical-Care Pain Observation Tool (CPOT) is one of the two behavioural pain scales suggested for clinical use. Still, no study has described the evaluations of its use in ICU daily practice. OBJECTIVE: To describe the nurses' evaluation of the feasibility, clinical relevance and satisfaction with the CPOT use 12 months after its implementation in the ICU. METHOD: A descriptive design was used. It was conducted in the medical-surgical ICU of a university affiliated setting at Greenfield Park (Québec, Canada). A self-administered evaluation questionnaire including four sections (i.e. feasibility, clinical relevance, satisfaction and socio-demographic information) was completed by ICU nurses who were all trained to use the CPOT. The questionnaires were completed anonymously. RESULTS: A total of 38 ICU nurses returned their completed questionnaire (63% participation rate). Regarding its feasibility, the majority rated the CPOT as quick to use, simple to understand and easy to complete (92-100%). According to clinical relevance, close to 70% of ICU nurses acknowledged that the CPOT had influenced their practice, but lower results (<50%) were found for effective communication of pain assessment findings with the physicians and other health professionals. More than 80% of ICU nurses were satisfied with its daily use. CONCLUSION: The CPOT use was deemed feasible and relevant in daily practice as per the nurses' evaluations but did not allow an effective communication with other ICU care team members. RELEVANCE TO CLINICAL PRACTICE: Training should be offered to all members of the ICU care team, and other implementation strategies should be explored as well to ensure optimal uptake of a pain assessment approach which impacts on their decision-making process for pain management.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".