Examining the implementation of the Healthy Kids Community Challenge
Bibliographic record
Abstract
Introduction: The Healthy Kids Community Challenge (HKCC) was a community-based, multi-stakeholder obesity prevention program aimed at children and youth in Ontario, which was funded and coordinated by the Ministry of Health and Long-term Care from 2015-2018. This project contributed to the process evaluation of the HKCC, which is being carried out by Public Health Ontario. The objective of my research was to identify and understand the barriers and facilitators to implementation of the HKCC, from the perspective of members of the Local Steering Committees (LSCs), who were directly involved in implementation. Methods: 10 questions from the 2018 LSC Survey were coded and thematically analyzed, using NVivo 11.0 software, according to the themes presented within Durlak and Dupre’s Ecological Framework for Effective Implementation. Themes were summarized according to barriers, challenges, factors associated with implementation, strategies communities used to engage partners and reach vulnerable populations, as well as positive experiences. The findings were presented in a report which featured a rich, qualitative account, accompanied by direct quotations and code frequency charts. Results: A number of perceived barriers to implementation were identified, including program structure (i.e., tight timelines, short duration), low SES (i.e., barriers related to lack of time, transportation and access to childcare, as well as difficulty accessing HKCC information) and geography and transportation (i.e., to access programming). Some important perceived facilitators of implementation included: funding, partnerships, HKCC messaging, and an inclusive, accessible approach to planning events. Participants expressed interest in sustaining programs and partnerships beyond the HKCC funding period, although the loss of provincial funding was identified as a challenge. Participants also noted potential for sustainability, particularly related to increased cross-sectoral collaboration and increased capacity for community-based health promotion. Implications: The results could be used to improve implementation of future community-based, multi-stakeholder health promotion programs. They might also offer insight into how to tailor the implementation process of large-scale community-based health promotion programs to local contexts.
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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".