Barriers, Strategies, and Resources to Thriving School Gardens
Bibliographic record
Abstract
OBJECTIVE: To identify school garden attributes and practices that most strongly contribute to garden use and sustainability and translate them into recommendations for improving garden-based nutrition education. DESIGN: Surveys were developed and administered to school stakeholders to assess the barriers, strategies, and resources for successful school garden-based nutrition education. A panel of school garden experts identified thriving school gardens. Logistic regression was used to identify which attributes predicted thriving school garden programs. SETTING: Approximately 109 schools across Greater Austin, TX. PARTICIPANTS: A total of 523 school teachers and 174 administrators. OUTCOMES: Barriers, strategies, and resources relevant to successful school gardening nutrition programs. RESULTS: Thriving school gardens were 3-fold more likely to have funding and community partner use (P = 0.022 and P = 0.024), 4 times more likely to have active garden committees (P = 0.021), available garden curriculum (P = 0.003), teacher training (P = 0.045), ≥ 100 students who used the garden annually (P = 0.047), and 12 times more likely to have adequate district and administrator support (P = 0.018). CONCLUSIONS AND IMPLICATIONS: Adequate administrative and district support is fundamental when implementing a school garden. Schools may benefit from finding additional funding, providing teacher garden training, providing garden curriculum, forming garden leadership committees, and partnering with local community organizations to improve garden-based nutrition education.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".