Exploring key elements of approaches that support childrens' preferences during painful and stressful medical procedures: A scoping review
Bibliographic record
Abstract
PROBLEM: Children undergoing medical procedures can experience pain and distress. While numerous interventions exist to mitigate pain and distress, the ability to individualize the intervention to suit the needs and preferences of individual children is emerging as an important aspect of providing family-centered care and shared decision making. To date, the approaches for supporting children to express their preferences have not been systematically identified and described. A scoping review was conducted to identify such approaches and to describe the elements that are included in them. ELIGIBILITY CRITERIA: Studies that (a) described approaches with the aim to support children to express their coping preferences during medical procedures; (b) included the option for children to choose coping interventions; (c) included a child (1--18 years). SAMPLE: Searches were conducted in December 2019 and November 2020 in the following databases: Cinahl, Embase, PubMed and Psycinfo. RESULTS: Thirteen studies were identified that included six distinct approaches. Four important key elements were identified: 1) Aid to express preferences or choice, 2) Information Provision, 3) Assessment of feelings/emotions, 4) Feedback/Reflection and Reward. CONCLUSIONS: Identified approaches incorporate components of shared decision-making to support children in expressing their preferences during medical procedures and treatments. IMPLICATIONS: Children undergoing medical procedures can be supported in expressing their coping needs and preferences by using components of shared decision-making.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".