Communication tools used in childhood obesity discussions: A scoping review
Bibliographic record
Abstract
BACKGROUND: Paediatric healthcare professionals (HCPs) play an essential role in the prevention and management of childhood obesity; however, many report considerable barriers to having weight-related conversations, including a lack of confidence. One way to address this is to use communication tools, such as charts, pictures and handouts. This scoping review's objective was to identify the extent and nature of available tools in the published literature that aimed to support HCPs in having positive weight-related conversations with children and families. METHODS: CINAHL, Medline and PsycINFO were systematically searched from 2005 to 2019. Articles were selected based on (a) use of an identified communication tool; (b) tool designed for use with children between the ages of 2 and 18 and/or their parents; (c) tool designed to be used in weight-related discussions; and (d) tool designed to be used by HCPs in a formal healthcare setting. RESULTS: Of the 3,596 articles yielded after deduplication, 13 unique communication tools were identified. Tools were grouped according to three themes: (a) help communicate child's weight status; (b) provide a communication framework for HCPs; and (c) actively engage children and/or parents in discussions. Many of the tools were recently developed and had not been evaluated in clinical practice. The clinical utility of each tool was evaluated. CONCLUSION: This study identified 13 communication tools HCPs could use when having weight-related discussions. However, there was a lack of evaluation and clinical utility. Only one tool had high clinical utility, and not all tools would be recommended for use in practice.
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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 | 0.034 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.034 | 0.036 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".