GROWING A BACKYARD GARDEN FOR HEALTH AND WELLBEING: A PUBLIC HEALTH NURSING BROCHURE
Bibliographic record
Abstract
A backyard garden may be an option for families concerned about healthy eating, food sensitivities, and food security. Despite this, backyard gardening is not a topic that nurse researchers have studied and there are few health promotion resources to support urban agriculture. The purpose of this project was to develop a brochure for novice gardeners on how to establish an urban backyard garden to meet their dietary needs. Specific objectives for this project were to identify and summarize professional and lay literatures on urban, backyard gardening in climate Zone 5b (Peterborough, Ontario) and to create a multimedia-friendly brochure for novice gardeners. This thesis consists of a literature review on gardening, nutrition, and the use of brochures as a knowledge translation tool. The Canadian Index for Wellbeing (CIW) and Lewin’s Three-Step Model for Change provided the conceptual framework for project. The methods chapter describes how the information was gathered and analyzed, and the findings chapter summarizes information to guide and support a family in growing their food. The final chapter includes a discussion on food security and household food self-sufficiency and their relationship to health, and the implications for nursing practice, nursing, education, and nursing research.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".