Development, evaluation and adaptation of a critical realism informed theory of procedural pain management in preterm infants: The <scp>PAIN‐Neo</scp> theory
Bibliographic record
Abstract
AIM: To present the development, evaluation and adaptation of the PAIN-Neo theory. DESIGN: Theory development. DATA SOURCES: A review of literature was conduct from 1980 to 2021. RESULTS: Using a critical realism paradigm, this paper presents the PAIN-Neo theory, which was developed from an analysis of existing theoretical perspectives on paediatric procedural pain, empirical studies conducted with preterm infants, and the research team's pain management expertise. The theory was then empirically tested and fine-tuned. IMPLICATIONS FOR NURSING: The PAIN-Neo theory highlights that the neonatal nurse is part of a larger picture as she is influenced by factors related to her unit, hospital and country of practice. This theory emphasizes the importance of parental involvement in pain management, which is consistent with family-centred nursing practices. CONCLUSION: The PAIN-Neo theory reflects the complexity of pain management nursing. This theory is innovative and specific enough to guide practice, structure research projects and contribute to the body of knowledge in the discipline of nursing.
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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 | 0.036 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".