Does The Children’s Fitness Tax Credit Make Children More Active? A Quasi-Experimental Study
Bibliographic record
Abstract
Importance: Canada’s Children's Fitness Tax Credit (CFTC) program offers households income tax credits to defray the cost of registering their children in an approved program of physical activity. There is limited evidence on the effects of fiscal policies, like the CFTC, that target the low rates of child physical activity seen in the US, Canada and elsewhere. Objective: To investigate the changes in children's physical activity participation and energy expenditure after the CFTC was introduced. Design: Using a quasi-experimental “difference-in-differences” design and multiple waves of the Canadian Community Health Surveys, we estimated the pre-post change in outcomes of individuals of CFTC-eligible age 12-15 (N=39,426) and compared that with the same pre-post change of those 16-21, who are CFTC ineligible (N=47,551). We generated estimates for both the entire population and separately by household income group. Setting: The residential population of Canada Participants: Individuals aged 12-21 years who participated in the Canadian Community Health Surveys Intervention: Implementation of the Children’s Fitness Tax Credit Program in Canada in January 2007 for children aged below 16 years. Main outcomes and measures: Participation in organized physical activities and total energy expended in leisure time physical activity. Results: The CFTC had modest and heterogeneous effects across income groups and specific physical activities. Physical activity participation increased by 3 percentage points among children from middle income families (p=0.02) and by 2 percentage points among those from high income families (p=0.07). These were accompanied by significant increases in child energy expenditure of 10.7% (p=0.02) and 7.6% (p=0.07), respectively. The CFTC increased rates of participation in 4 sports: basketball (all groups), hockey (only high income group), soccer (only middle income group) and skating (only low income group). Conclusion and Relevance: Following the CFTC, there was a modest increase in physical activity participation and energy expenditure among children from middle to high income families. Though modest, these increases may have important health benefits. The cost effectiveness of the CFTC remains an open question.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".