Supporting parents of preschool children in adopting a healthy lifestyle
Bibliographic record
Abstract
BACKGROUND: Childhood obesity is a public health epidemic. In Canada 21.5% of children aged 2-5 are overweight, with psychological and physical consequences for the child and economic consequences for society. Parents often do not view their children as overweight. One way to prevent overweight is to adopt a healthy lifestyle (HL). Nurses with direct access to young families could assess overweight and support parents in adopting HL. But what is the best way to support them if they do not view their child as overweight? A better understanding of parents' representation of children's overweight might guide the development of solutions tailored to their needs. METHODS/DESIGN: This study uses an action research design, a participatory approach mobilizing all stakeholders around a problem to be solved. The general objective is to identify, with nurses working with families, ways to promote HL among parents of preschoolers. Specific objectives are to: 1) describe the prevalence of overweight in preschoolers at vaccination time; 2) describe the representation of overweight and HL, as reported by preschoolers' parents; 3) explore the views of nurses working with young families regarding possible solutions that could become a clinical tool to promote HL; and 4) try to identify a direction concerning the proposed strategies that could be used by nurses working with this population. First, an epidemiological study will be conducted in vaccination clinics: 288 4-5-year-olds will be weighed and measured. Next, semi-structured interviews will be conducted with 20 parents to describe their representation of HL and their child's weight. Based on the results from these two steps, by means of a focus group nurses will identify possible strategies to the problem. Finally, focus groups of parents, then nurses and finally experts will give their opinions of these strategies in order to find a direction for these strategies. Descriptive and correlational statistical analyses will be done on the quantitative survey data using SPSS. Qualitative data will be analyzed using Huberman and Miles' (2003) approach. NVivo will be used for the analysis and data management. DISCUSSION: The anticipated benefits of this rigorous approach will be to identify and develop potential intervention strategies in partnership with preschoolers' parents and produce a clinical tool reflecting the views of parents and nurses working with preschoolers' parents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".