Reliability and Validity of the Perceived Neighborhood Food Environment Scale
Bibliographic record
Abstract
In order for people to make healthy food choices, the food environment needs to be improved and residents must be aware of it. This research aimed to assess the reliability and validity of the Perceived Neighborhood Food Environment (PNFE) scale, which assesses participants’ perceptions of the present condition of their food environment. Data from the Survey on the Present Condition and Consciousness of Dietary Education conducted by the Cabinet Office of Japan in 2010 were used, and 1,853 participants were included. We performed an exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), and examined internal consistency and the criterion-related validity of the PNFE. The PNFE comprised two factors: “regional food culture” (5 items) and “physical availability of food” (3 items). The model fitness indices were good (GFI = .97, AGFI = .95, CFI = .96, RMSEA = .073) and Cronbach’s α was .77 for the whole scale. Reasonable results were obtained for criterion-related validity. We confirmed the reliability and validity of the PNFE scale. By utilizing the scale for future research in other countries, its reliability and validity for a wider range of residents will likely be confirmed. In addition, consideration should be given to the items used for confirming the validity of the scale in the next study to ensure that they are appropriate for other countries included in the research.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".