Assessment and management of pediatric pain based on the opinions of health professionals.
Bibliographic record
Abstract
The present study characterized the opinions of health professionals about strategies for assessing and managing pediatric pain in a public teaching hospital. The sample consisted of 92 health professionals who worked in pediatric wards, pediatric intensive care, and neonatal intensive care. The sample included 45 doctors, 18 nurses, 16 psychologists, eight physiotherapists, and five occupational therapists. Data were collected through a self-administered questionnaire that included 22 open questions on the following topics: pain assessment, pharmacological management, and non-pharmacological interventions. Each area was analyzed with regard to actions, resources, gaps, and needs. The questionnaire was developed based on the principles of Strategic Planning. Two trained researchers analyzed the thematic content of all of the responses. With regard to actions and resources, 33% of the respondents mentioned the use of instruments for pain assessment, 73% reported that they prescribed pharmacological treatments for symptoms of pain, and 26% reported the use of non-pharmacological interventions for the relief of pain. The professionals predominantly reported a lack of training for pain assessment and management, standardized protocols, and human and material resources. Consequently, 96% of the professionals reported the necessity for educational training and standardized implementation guidelines for pain assessment services. These findings provide a baseline of the health professional's opinions of pain issues, which are essential for implementing and increasing pain assessment and management policies institutionally.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".