Pain Prevalence in a Pediatric Hospital: Raising Awareness during Pain Awareness Week
Bibliographic record
Abstract
BACKGROUND: Despite the evidence and availability of numerous validated pain assessment tools and pain management strategies for infants and children, their use remains inconsistent in clinical practice. OBJECTIVES: To describe the prevalence of pain, pain assessment and pain management practices at a tertiary pediatric hospital in Canada. METHODS: The cross-sectional study design involved a combination of interviews with children and⁄or caregivers, and chart audits in five inpatient units. Information regarding pain intensity, painful procedures and pain management strategies was obtained from children and⁄or caregivers by interview. Patient charts were reviewed for information regarding pain assessment, pain scores, and pharmacological and nonpharmacological interventions. RESULTS: Sixty-two children (four days to 17 years of age) participated. Most children or their caregivers (n=51 [84%]) reported that pain was experienced during their hospitalization, with 40 (66%) reporting their worst pain as moderate or severe. Almost one-half reported analgesics were administered before or during their most recent painful procedure. Nineteen (32%) reported sucrose, topical anesthetics or nonpharmacological interventions were used; however, they were documented in only 17% of charts. Pain scores were documented in 34 (55%) charts in the previous 24 h. The majority of the children or their caregiver (n=44 [71%]) were satisfied with pain management at the study hospital. CONCLUSIONS: Most infants and children had experienced moderate or severe pain during their hospitalization. Analgesics were frequently used, and although nonpharmacological strategies were reported to be used, they were rarely documented. Most parents and children were satisfied with their pain management.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".