Priority topics for child and family health research in community-based paediatric health care according to caregivers and health care professionals
Bibliographic record
Abstract
Background: Patient-oriented research (POR) aligns research with stakeholders' priorities to improve health services and outcomes. Community-based health care settings offer an opportunity to engage stakeholders to determine the most important research topics to them. Our objectives were to identify unanswered questions that stakeholders had regarding any aspect of child and family health and prioritize their 'top 10' questions. Methods: We followed the James Lind Alliance (JLA) priority setting methodology in partnership with stakeholders from the Northeast Community Health Centre (NECHC; Edmonton, Canada). We partnered with stakeholders (five caregivers, five health care professionals [HCPs]) to create a steering committee. Stakeholders were surveyed in two rounds (n = 125 per survey) to gather and rank-order unanswered questions regarding child and family health. A final priority setting workshop was held to finalize the 'top 10' list. Results: Our initial survey generated 1,265 submissions from 100 caregivers and 25 HCPs. Out of scope submissions were removed and similar questions were combined to create a master list of questions (n = 389). Only unanswered questions advanced (n = 108) and were rank-ordered through a second survey by 100 caregivers and 25 HCPs. Stakeholders (n = 12) gathered for the final workshop to discuss and finalize the 'top 10' list. Priority questions included a range of topics, including mental health, screen time, COVID-19, and behaviour. Conclusion: Our stakeholders prioritized diverse questions within our 'top 10' list; questions regarding mental health were the most common. Future patient-oriented research at this site will be guided by priorities that were most important to caregivers and HCPs.
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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 | 0.199 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".