Healthy community development: Creating and assessing tools for planning and designing healthy communities in British Columbia
Bibliographic record
Abstract
The health of our communities is of increasing concern to professionals and various health organisations. The design of our built environment influences most of our daily activities which in turn influence our health. This thesis explores the impacts of planning on the health of the community. Health organisations such as WHO, PHSA, etc. have created frameworks which relate design to health but these frameworks are quite general in nature. This thesis focuses on the relationship of health to neighbourhood planning with a focus on two neighbourhoods in Nanaimo. This thesis aims to help in gaining a more complete understanding of the various factors present in these neighbourhoods and what can be improved in terms of the existing policies and guidelines for developing a healthier future. The approach used here is mainly secondary data collection and the analysis of the collected data to help design an improved healthy community framework. This thesis specifically deals with providing an updated framework for planning which can be easily used by professionals such as planners, developers, municipal officials and health officials. The proposed framework aims to commence a guiding structure for the future of planning and design guidelines.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".