Status report - The Public Health and Planning 101 project: strengthening collaborations between the public health and planning professions
Bibliographic record
Abstract
INTRODUCTION: Land use planning is a complex field comprised of legislation, policies, processes and tools. A growing body of evidence supports the relationship between land use planning decisions, community design and health. The built environment has been shown to be associated with physical inactivity, obesity, cardiovascular disease, respiratory disease and mental illness. Consequently, there is a growing interest within public health to work with planners on land use planning initiatives such as official plans and transportation master plans. METHODS: Two surveys were developed: one for public health professionals and the other for planning professionals (survey questions available upon request to the corresponding author). The surveys were pilot tested in two separate focus group sessions with public health and planning professionals. Focus group volunteers helped to validate the surveys by verifying survey questions, design and overall flow. RESULTS: In early 2012, 304 public health professionals and 301 planning professionals completed the two separate surveys, comprising the total survey respondents for each respective profession used to calculate proportions. The survey results represent a convenience sample and are not generalizable to the entire population of public health and planning professionals in Ontario. Results compare survey responses from both groups where appropriate. Most respondents worked either as public health staff (78%) or planners/senior planners (58%). A smaller percentage of public health and planning professionals worked either as managers (15% and 11%, respectively) or directors (5% and 9%, respectively). CONCLUSION: Health is associated with how communities are planned and built, and the services and resources provided within them. Inspired by the results of our survey and based on user feedback from the pilot tests, a free online training program entitled "Public Health and Planning 101: An Online Course for Public Health and Planning Professionals to Create Healthier Built Environments" was launched in 2016 by OPHA as a collaborative project with OPPI and PHAC. This course is designed to bridge the gaps between the two professions, as well as provide greater opportunities for developing collaborative partnerships to help create and foster healthy built environments.
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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 | 0.036 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".