Bibliographic record
Abstract
Background. Tools and resources (TRs) can help to prevent obesity in children, particularly in settings that are accessible to families and well-aligned with chronic disease prevention, such as primary care. To date, little is known about the TRs that primary care providers (PCPs) currently use to prevent childhood obesity and how they can be evaluated, and if brief and novel eHealth (electronic Health) tools can be applied to help parents prevent childhood obesity when delivered in primary care. Objectives. To (i) pilot test a new method to evaluate TRs that PCPs currently use for preventing childhood obesity in primary care, and report a preliminary descriptive assessment of these TRs, and (ii) develop, refine, and pilot test a brief eHealth tool delivered in primary care to help parents prevent obesity in children. Methods. This doctoral thesis includes a mixed methods study (Study 1) and a multi-phased study (Study 2). The first study included individual semi-structured interviews with PCPs (Phase I) and evaluated currently used TRs across three assessment checklists (Phase II). Feedback was obtained from PCPs on our coding scheme and checklist data at follow-up (Phase III). The second study included the development of a parent-based digital screening, brief intervention and referral to treatment (SBIRT) (Phase I), which was subsequently refined using focus groups with parents and stakeholders (Phase II). The modified version was pilot tested using a randomized controlled trial in primary care to assess feasibility and preliminary impact (Phase III). Results. For study 1, criteria on the checklists overlapped with PCPs’ perceptions of the suitability of TRs, but did not reflect the logistical factors that impacted their use. PCPs (n=19) reported using 15 TRs, most of which scored ‘adequate’ on the three checklists. For study 2, the SBIRT was developed by our research team and industry partners based on existing models and contemporary literature on children’s lifestyle behaviors. Refinements to the SBIRT were guided by feedback from five focus groups with health care professionals (n=20), parents (n=10), and researchers (n=8); participants viewed the SBIRT as a practical, well-designed eHealth tool, but suggested improvements to specific elements, such as weight-related terms that may elicit negative reactions from parents. Lastly, the SBIRT was pilot tested with parents (n=226) in primary care. The level of recruitment (n=226/268; 84.3%) and the proportion of parents who self-selected resources (n=194/226; 85.8%) within the SBIRT supported feasibility. At one-month follow-up, a greater proportion of parents with unhealthy weight children reported discussing weight with their pediatrician compared to those with healthy weight children (2=15.4; p<0.001). Conclusions. These studies provided a unique assessment and understanding of TRs that are used to prevent childhood obesity in primary care. The mixed methods evaluation of TRs demonstrated the usefulness of combining feedback from front-line providers with objective assessment data. Our preliminary assessment of TRs that PCPs currently use in Alberta demonstrated there is room for improvement, particularly with respect to readability levels and lack of content diversity beyond nutrition and physical activity. Based on feedback from focus group participants and pilot testing of our newly-developed eHealth tool, the SBIRT was feasible in primary care and may help to nudge parents towards accessing and using TRs that can have a positive impact on children’s lifestyle behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".