Unpacking vertical and horizontal integration: childhood overweight/obesity programs and planning, a Canadian perspective
Bibliographic record
Abstract
BACKGROUND: Increasingly, multiple intervention programming is being understood and implemented as a key approach to developing public health initiatives and strategies. Using socio-ecological and population health perspectives, multiple intervention programming approaches are aimed at providing coordinated and strategic comprehensive programs operating over system levels and across sectors, allowing practitioners and decision makers to take advantage of synergistic effects. These approaches also require vertical and horizontal (v/h) integration of policy and practice in order to be maximally effective. DISCUSSION: This paper examines v/h integration of interventions for childhood overweight/obesity prevention and reduction from a Canadian perspective. It describes the implications of v/h integration for childhood overweight and obesity prevention, with examples of interventions where v/h integration has been implemented. An application of a conceptual framework for structuring v/h integration of an overweight/obesity prevention initiative is presented. The paper concludes with a discussion of the implications of vertical/horizontal integration for policy, research, and practice related to childhood overweight and obesity prevention multiple intervention programs. SUMMARY: Both v/h integration across sectors and over system levels are needed to fully support multiple intervention programs of the complexity and scope required by obesity issues. V/h integration requires attention to system structures and processes. A conceptual framework is needed to support policy alignment, multi-level evaluation, and ongoing coordination of people at the front lines of practice. Using such tools to achieve integration may enhance sustainability, increase effectiveness of prevention and reduction efforts, decrease stigmatization, and lead to new ways to relate the environment to people and people to the environment for better health for children.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".