Evaluating the Impact of the Healthy Kids Community Challenge (HKCC) on Physical Activity of Older Youth
Bibliographic record
Abstract
(1) Background: The Healthy Kids Community Challenge (HKCC) was a community-based obesity prevention intervention funded by the Government of Ontario (Canada). (2) Methods: A quasi-experimental design was used to examine the impact of the HKCC on physical activity (PA) outcomes using both repeat cross-sectional (T1 2014-2015, n = 31,548; T2 2015-2016, n = 31,457; and T3 2016-2017, n = 30,454) and longitudinal data (n = 3906) from the COMPASS study. Grade 9-12 students in HKCC communities were placed into one of three intervention groups [T2 data collection post-HKCC finishing (IG1), T2 data collection during HKCC (IG2), and T2 data collection pre-HKCC starting (IG3)], Ontario students in non-HKCC communities were Control Group 1 (CG1) and Alberta students were Control Group 2 (CG2). (3) Results: Repeat cross-sectional results show over time the HKCC had no significant impact on PA outcomes in any of the intervention groups. Longitudinal results show a significant decrease in time spent in moderate-to-vigorous PA (IG2: -3.15 min/day) between T1 and T3 in IG2. (4) Conclusions: These results suggest the HKCC did not have an impact on improving PA outcomes among older youth in HKCC communities. Moving forward, there is a need to provide effective and sustainable interventions to promote PA among older youth.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".