Patient satisfaction with pain management by nurses in postoperative cardiac patients
Bibliographic record
Abstract
Patients undergoing cardiovascular surgery have reported considerable unrelieved \npain (Watt-Watson, et al., 2001). Nurses’ pain-related knowledge and practices may be \nrelated to patient satisfaction with pain. The purpose of this study is to examine the \nrelationship between nurses’ knowledge about pain for postoperative cardiac patients and \nquality and intensity of patient’s pain. The study will be a modified replication study of \nWatt-Watson et al. 2001. The sample will include 30 registered nurses working with \npost-operative cardiac patients and 60 post-operative cardiac patients in a Midwest \nhospital. The Toronto Pain Management Inventory (TPMI) will be used to measure \nnurses’ pain knowledge. The McGill Pain Questionnaire-Short Form (MPQ-SF) will be \nused to measure the quality and intensity of patients’ pain. Permission will be obtained \nfrom Ball State University and the participating hospital. The findings will provide \ninformation for nurses who manage cardiac surgical 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".