Can We Improve Parents’ Management of their Children’S Postoperative Pain at Home?
Bibliographic record
Abstract
BACKGROUND: Thousands of children undergo surgery each year, and a shift toward same-day surgeries and decreased lengths of hospital stay results in parents being increasingly responsible for their child's postoperative care. Recent studies have tested interventions designed to improve parent management of their children's postoperative pain at home, but progress in this area has been limited by a lack of synthesis of these findings. OBJECTIVE: To conduct a systematic review of interventions to improve parent management of children's postoperative pain at home. METHODS: Articles evaluating interventions to improve management of their children's postoperative pain were identified using a library scientist-designed search strategy applied in EMBASE, PubMed, CINAHL and PsycINFO. Two independent raters assessed each study for eligibility and extracted data. RESULTS: Of the 147 articles identified for the review, eight met the inclusion criteria. Interventions included pain education, training in pain assessment, education on distraction, instruction in around-the-clock dosing and nurse coaching. Overall, results of comparisons of pain intensity and analgesic administration were modest. The intervention with the largest effect size was instruction in around-the-clock dosing, either alone or in combination with nurse coaching. Results of studies investigating pain assessment, pain education and distraction trials revealed small to medium effect sizes. CONCLUSIONS: Results of trials investigating interventions to improve parent management of their children's postoperative pain at home were modest. Future studies should further examine barriers and facilitators to pain management to design more effective interventions.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".