Critical Care Nurses' Pain Assessment and Management Practices: A Survey in Canada
Bibliographic record
Abstract
BACKGROUND: Regular pain assessment can lead to decreased incidence of pain and shorter durations of mechanical ventilation and stays in the intensive care unit. OBJECTIVES: To document knowledge and perceptions of pain assessment and management practices among Canadian intensive care unit nurses. METHODS: A self-administered questionnaire was mailed to 3753 intensive care unit nurses identified through the 12 Canadian provincial/territorial nursing associations responsible for professional regulation. RESULTS: A total of 842 nurses (24%) responded, and 802 surveys could be evaluated. Nurses were significantly less likely (P < .001) to use a pain assessment tool for patients unable to communicate (267 nurses, 33%) than for patients able to self-report (712 nurses, 89%). Significantly fewer respondents (P < .001) rated behavioral pain assessment tools as moderately to extremely important (595 nurses, 74%) compared with self-report tools (703 nurses, 88%). Routine (>50% of the time) discussion of pain scores during nursing handover was reported by 492 nurses (61%), and targeting of analgesia to a pain score or other assessment parameters by physicians by 333 nurses (42%). Few nurses (n = 235; 29%) were aware of professional society guidelines for pain assessment and management. Routine use of a behavioral pain tool was associated with awareness of published guidelines (odds ratio, 2.5; 95% CI, 1.7-3.7) and clinical availability of the tool (odds ratio, 2.6; 95% CI, 1.6-4.3). CONCLUSIONS: A substantial proportion of intensive care unit nurses did not use pain assessment tools for patients unable to communicate and were unaware of pain management guidelines published by professional societies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".