Bibliographic record
Abstract
Individuals with obesity often appear reluctant to undertake dietary changes.Evaluating the reasons for this reluctance, as well as appropriate policy responses, is hampered by a lack of data on behavioral response to dietary advice.I use household scanner data to estimate food purchase response to a diagnosis of diabetes, a common complication of obesity.I infer diabetes diagnosis within the scanner data from purchases of glucose testing products.Households engage in statistically significant but small calorie reductions following diagnosis.The changes are sufficient to lose 4 to 8 pounds in the first year, but are only about 10% of what would be suggested by a doctor.The scanner data allows detailed analysis of changes by food type.In the first month after diagnosis, healthy foods increase and unhealthy foods decrease.However, only the decreases in unhealthy food persist.Changes are most pronounced on large, unhealthy, food categories.Those individuals whose pre-diagnosis diet is concentrated in one or a few foods groups show bigger subsequent calorie reductions, with these reductions occurring primarily occurring in these largest food groups.I suggest the facts may be consistent with a psychological framework in which rule-based behavior change is more successful.I compare the results to a policy of taxes or subsidies.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".