Extending the Theory of Planned Behavior for Explaining Dietary Quality: The Role of Financial Scarcity and Food Insecurity Status
Bibliographic record
Abstract
Objective To examine whether an extended Theory of Planned Behavior (TPB) that included finance-related barriers better explained dietary quality. Design Cross-sectional survey. Participants One-thousand and thirty-three participants were included from a Dutch independent adult panel. Main Outcome Dietary quality. Analysis Five TPB models were assessed: a traditional TPB, a TPB that included direct associations between attitude and subjective norm with dietary quality, a TPB that additionally included financial scarcity or food insecurity, and a TPB that additionally included financial scarcity and food insecurity simultaneously. Structural relationships among the constructs were tested to compare the explanatory power. Results The traditional TPB showed poorest fit (χ 2 /degrees of freedom = 11; comparative fit index = 0.75; root mean square error of approximation [95% confidence interval], 0.10 [0.091–0.12]; standardized root mean square residual = 0.049), the most extended TPB (including both financial scarcity and food insecurity) showed best fit (χ 2 /degrees of freedom = 3.3; comparative fit index = 0.95; root mean square error of approximation [95% confidence interval], 0.050 [0.035–0.065]; standardized root mean square residual = 0.018). All 5 structure models explained ∼42% to 43% of the variance in intention; however, the variance in dietary quality was better explained by the extended TPB models, including food insecurity and/or financial scarcity (∼22%) compared with the traditional TBP (∼7%), indicating that these models better explained differences in dietary quality. Conclusions and Implications These findings highlight the importance of accounting for finance-related barriers to healthy eating like financial scarcity or food insecurity to better understand individual dietary behaviors in lower socioeconomic groups.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".