An early implementation assessment of Ontario’s Healthy Kids Community Challenge: results from a survey of key stakeholders
Bibliographic record
Abstract
BACKGROUND: In Ontario Canada, the Healthy Kids Community Challenge (HKCC) is a program intended to reduce the prevalence and prevent childhood overweight and obesity through community-based initiatives to improve health behaviours. Guided by the RE-AIM framework and Durlak and DuPre's Ecological Framework for Understanding Effective Implementation, the evaluation focused on two objectives: 1) to describe the organization of the program at the community level; and, 2) to identify opportunities for improvement through an early assessment of factors contributing to implementation. METHODS: Participants (n = 320) - members of the HKCC local steering committee, including the local project manager - completed a cross-sectional survey using SurveyMonkey and descriptive statistics were calculated. A sample (20%) of qualitative open-ended responses was thematically analyzed. RESULTS: Results indicated strong respondent agreement that the HKCC enhanced individual knowledge of access to health-promoting programs (88.3%) and messaging regarding healthy behaviours for healthy kids, with less for its effectiveness in reducing weight (53.1%). There was a high-level of adherence to HKCC social marketing messages and overall program structure, with few Local Project Manager reports of adaptations to theme one (9.2%) and theme two messages (15.4%). Fewer Local Project Managers (50%) reported the existence of private partnerships. While most respondents agreed they had the appropriate information to complete mandatory reporting, the usefulness of the HKCC online networking platform was in question (only 47% of Local Project Managers agreed that it was useful). Results reveal sufficient funding from the province to support program implementation, with a moderate level of local political commitment (63% of respondents). CONCLUSIONS: Results indicate that the HKCC was considered beneficial for enhancing access to health promoting programs, could be feasibly implemented with adherence to centrally-developed social marketing messages, and was amendable to local adaptation. Despite this, few private partnerships were reported. Going forward, there is opportunity to further evaluate factors contributing to HKCC program implementation, particularly as it relates to buy-in from intervention providers, and strategies for forming private sector partnerships to support long-term program sustainability.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".