Fostering positive weight‐related conversations between health care professionals, children, and families: Development of a knowledge translation Casebook and evaluation protocol
Bibliographic record
Abstract
BACKGROUND: Health care professionals (HCPs) must communicate with children and families about weight management in a sensitive and nonstigmatizing manner. However, HCPs receive little training in weight-related communication and have requested resources, but these are scarce. This article details the development process of a knowledge translation (KT) Casebook and outlines the proposed protocol for its implementation and evaluation. METHODS: Guided by the knowledge-to-action cycle, a KT Casebook aimed at HCPs integrated findings from a comprehensive scoping review with experiential and contextual evidence gathered through stakeholder workshops to provide guidance to HCPs communicating with children and families around weight-related issues. It was structured around five questions: (a) Who should participate in weight-related discussions? (b) When and how should the topic of weight be broached? (c) What terminology should be used? (d) How can HCPs enhance family engagement in weight-related discussions? (e) What specific communication techniques have been recommended? Checklists, evidence summaries, case studies, sentence starters, simulations, and other resources were clustered under each question. A dissemination strategy was developed using KT best practices and a pilot evaluation protocol designed. RESULTS: The Casebook was launched in November 2017 and has been presented at pediatric rehabilitation and obesity conferences. A communication strategy targeted multidisciplinary experts and organizations with weight management within their scope of practice. These efforts have resulted in 2,497 downloads across five countries to date. CONCLUSIONS: A practical and engaging KT Casebook was created to help foster positive weight-related conversations between HCPs, children, and their families. A pilot implementation study examining the impact of the Casebook on clinical practice will provide critical information for embedding the Casebook in a range of health care settings.
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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 | 0.136 | 0.153 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".