Identifying research priorities with children, youth, and families: A scoping review
Bibliographic record
Abstract
Increased patient advocacy has resulted in a shift toward more active patient engagement in the research. A scoping review was conducted to explore the literature on healthcare research priority settings wherein children, youths, or their families were involved in the priority-setting process. Six databases including MEDLINE, CINAHL, PsycINFO, Embase, Web of Science, and Global Health and the James Lind Alliance website were searched up until September 2019. All primary studies involving children (<18 years of age) or families in developing research priorities in health care were included. All retrieved references were uploaded into Covidence, and two independent reviewers screened the search results. Descriptive thematic analysis was used to identify common themes. A total of 30 studies with 4247 participants were included. Less than half of the participants ( n = 1237, (33%) were pediatric patients and their families. A total of 455 research priorities were identified. Three common themes emerged: (i) quality of care delivery, (ii) self-efficacy in health behaviors, and (iii) community engagement in care. This scoping review revealed priority research health topics from the perspectives of children, youths, or their families. The findings may be used as a foundation for future research to improve the health outcomes of children, youths, or their families according to their identified priorities.
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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 | 0.038 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 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".