Experience of Registered Nurses of Postoperative Pain Assessment Using Objective Measures among Children at Effia Nkwanta Regional Hospital in Ghana
Bibliographic record
Abstract
IntroductionSurgical operations are inevitable in alleviating certain disease conditions, however, surgical procedures are associated with pain.4][5] Every individual is entitled to pain management.][8][9] Pain assessment involves the use of subjective and objective measures and the subjective measures involve the use of self-reports where patients verbalize or describe their pain.Objective measures comprising behavioural and physiological measures are commonly used to assess children's pain.Behavioural measures involve observing how a child behaves in response to pain such as facial expressions, crying, body postures and movements. 10Physiological measures include assessment of heart rate, blood pressure, respiration, oxygen saturation, palmer sweating and temperature. 11For pain, assessment to be practical, and consistent pain assessment tools and guidelines are used.Some of the common pain assessment tools used among children include Faces Pain Scale-Revised (FPS-R), the Wong-Baker Faces Scale and the Oucher Scale, the Face, Legs, Activity, Cry and Consolability (FLACC), the Children's Hospital of Eastern Ontario Pain Scale (CHEOPS), the Toddler-Preschooler Postoperative Pain Scale (TPPPS), and the Parents' Postoperative Pain Rating Scale (PPPRS).3][14][15] Despite the knowledge
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".