Bibliographic record
Abstract
Food environments with high fast food access may increase risk for poor diet. As much as 62% of the Canadian diet consists of highly processed convenience foods, including food consumed at so-called ‘fast food’ outlets (Ogilvie & Eggleton, 2016). These types of limited service restaurants offer convenience foods, which are typically both calorically dense and high in fat, salt, and sugar. This thesis contributes a new measure of fast food access for Canadian neighbourhoods and examines whether neighbourhood fast food access is associated with increased fast food consumption. Fast food retail outlets were extracted from the Statistics Canada Business Register and mapped in a geographic information system (ArcGIS) by their geocoded location. Absolute and relative fast food access measures were created using 1000m and 3000m network buffers in the neighbourhoods of respondents of the 2015 Canadian Community Health Survey-Nutrition (N = 10,182). Fast food consumption was measured using a question from the survey’s 24-hour dietary recall. 12.7% of adult Canadians reported eating in a fast food restaurant the previous day, with this number varying by province and city. Despite significant variation in Canadian adults’ reporting of fast food consumption across provinces and cities, there was no conclusive influence of the neighbourhood food environment (at either 1000m or 3000m) on fast food consumption. Factors associated with fast food consumption in multivariate analyses were young age (18-24), being male, single, and in the workforce. Results speak to the scale at which fast food consumption cultures may be created. Neighbourhood access may matter less than the surrounding urban environment as a whole in determining fast food consumption
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".