Putting family into family-based obesity prevention: enhancing participant engagement through a novel integrated knowledge translation strategy
Bibliographic record
Abstract
BACKGROUND: With 1 in 4 Canadian preschoolers considered overweight or obese, identifying risk factors for excess weight gain and developing effective interventions aimed at promoting healthy weights and related behaviours among young children have become key public health priorities. Despite the need for this research, engaging and maintaining participation is a critical challenge for long-term, family-based studies. The aim of this study is to describe the implementation and evaluation of a parent-only advisory council designed to engage participants in the implementation and evaluation of a longitudinal, family-based obesity prevention intervention. METHODS: A Family Advisory Council (n = 14 parents, 70% mothers, 64% white), was established to engage participant stakeholders in decisions related to research protocols and strategies to engage and sustain family participation. Using a mixed methods approach, including a participant survey and focus group, we examined the council members' perceptions of their role and the impact this novel integrated Knowledge Translation (iKT) strategy had on the Guelph Family Health Study (GFHS), a longitudinal family-based study. RESULTS: All members of the Family Advisory Council felt the topics discussed were appropriate, felt that their opinions were valued and that their suggestions have had an impact and direct benefit on the GFHS. The addition of the Family Advisory Council led to changes in study protocol (i.e. creation of more detailed intervention emails, creation of kid-friendly accelerometer bands) that may have contributed to the high retention rate of the GFHS (95% at 6-month follow-up). CONCLUSIONS: Engaging parents as research partners in family-based research studies may be an effective way to increase participant engagement and study retention.
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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 | 0.039 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".