Item selection and content validity of the Critical‐Care Pain Observation Tool for non‐verbal adults
Bibliographic record
Abstract
AIM: This paper is a report of the item selection process and evaluation of the content validity of the Critical-Care Pain Observation Tool for non-verbal critically ill adults. BACKGROUND: Critically ill patients experience moderate to severe pain in the intensive care unit. While critical care clinicians strive to obtain the patient's self-report of pain, many factors compromise the patient's ability to communicate verbally. Pain assessment methods often need to match the communication capabilities of the patient. In non-verbal patients, observable behavioural and physiological indicators become important indices for pain assessment. METHOD: A mixed method study design was used for the development of the Critical-Care Pain Observation Tool in 2002-2003. More specifically, a four-step process was undertaken: (1) literature review, (2) review of 52 patients' medical files, (3) focus groups with 48 critical care nurses, and interviews with 12 physicians, and (4) evaluation of content validity with 17 clinicians using a self-administered questionnaire. RESULTS: Item selection was derived from different sources of information which were convergent and complementary in their content. An initial version of the Critical-Care Pain Observation Tool was developed including both behavioural and physiological indicators. Because physiological indicators received more criticism than support, only the four behaviours with content validity indices >0.80 were included in the Critical-Care Pain Observation Tool: facial expression, body movements, muscle tension and compliance with the ventilator. CONCLUSION: Item selection and expert opinions are relevant aspects of tool development. While further evaluation is planned, the Critical-Care Pain Observation Tool appears as a useful instrument to assess pain in critically ill patients.
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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 | 0.051 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".