Practice of Epidemiology Construct Validation of 4 Food-Environment Assessment Methods: Adapting a Multitrait-Multimethod Matrix Approach for Environmental Measures
Bibliographic record
Abstract
Few studies have assessed the construct validity of measures of neighborhood food environment, which remains amajor challenge in accurately assessing food access. In this study, we adapted a psychometric tool to examine the construct validity of 4 such measures for 3 constructs. We used 4 food-environment measures to collect objective data from 422 Ontario, Canada, food stores in 2010. Residents ’ perceptions of their neighborhood food environ-ment were collected from 2,397 households between 2009 and 2010. Objective and perceptual data were aggre-gated within buffer zones around respondents ’ homes (at 250 m, 500 m, 1,000 m, and 1,500 m). We constructed multitrait-multimethod matrices for each scale to examine construct validity for the constructs of food availability, food quality, and food affordability. Convergent validity between objective measures decreased with increasing geographic scale. Convergent validity between objective and subjective measures increased with increasing geo-graphic scale. High discriminant validity coefficients existed between food availability and food quality, indicating that these two constructs may not be distinct in this setting. We conclude that the construct validity of food environ-ment measures varies over geographic scales, which has implications for research, policy, and practice. food availability; food environment; food supply; nutrition; nutrition policy; psychometrics; residence characteristics
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 | 0.310 | 0.376 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".