The examination of nurse, organizational and infant factors associated with effective procedural pain care in hospitalized infants /
Bibliographic record
Abstract
Background. Hospitalized infants still experience pain from regularly performed tissue-damaging procedures. Nurses have knowledge to manage the pain but do not apply that knowledge. There is agreement that hospital organizational factors influence quality of care outcomes and some indication that patient factors may influence level of care. Objective. This study examined the factors from a conceptual model entitled Knowledge Use for Pain Care. These were (1) nurse, (2) organizational and (3) infant factors in relation to nurses' management of tissue-damaging procedures in hospitalized infants. Research design. A prospective study using two neonatal intensive care units in two Canadian provinces in 2005 was used. Participants. Ninety-three nurses who performed 170 tissue-damaging procedures. Measures. Nurse demographic data included education, and years of experience. Nurses' knowledge of pain was measured using the new Pain Knowledge and Use instrument. Organizational factors were measured using the nurse-doctor Collaboration and Satisfaction about Care Decisions Scale (Baggs, 1994), and the Environmental Complexity Scale (O'Brien, Irvine, Peereboom & Murray, 1997). Infant factors included age and intensity of care required. Actual pain care was measured by a scorecard of nurses' assessment, management and documentation for the tissue-damaging procedure. Estimates of professional nurse, organizational and infant effects on nurses' pain care for the tissue-damaging procedures were derived from general estimating equations. Results. Higher procedural pain care was associated with higher nurse doctor collaboration (odds ratio {OR}, 1.44; 95% confidence intervals {CI} 1.05-1.98), infants with higher intensity of care (OR, 1.21; 95% CI, 1.06-1.39), unanticipated increases in the work of nurse assignments (OR, 1.55; 95% CI, 1.04-2.30). Nurse's level of pain knowledge was adequate but did not translate into actual care for tissue-damaging procedures. Conclusion. The results suggest that the model was partly supported. In particular the importance of nurse-physician collaboration in pain care delivery, a variable amenable to intervention and further study was significant. Overall, the factors that influence nurse's use of their pain knowledge to provide better care remain a complex 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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".