Bibliographic record
Abstract
Obesity is a significant public health issue, and many public health programs and interventions have been developed to address it. Healthy eating at the workplace is a key component of obesity pr??vention, however, evidence of its cost and effectiveness is largely missing. This dissertation took a multi-faceted approach to the evaluation of a workplace healthy-eating intervention implemented in the Alberta Health Services: Calgary Area in September 2006. The first epidemiological study provides a snapshot of the relationship between healthy eating and obesity at the Canadian population level. National dietary patterns were derived from the Canadian Community Health Survey (CCHS) Cycle 2.2 in 2004 and the relationship between obesity and healthy eating, as reflected by the adherence to the Canada's Food Guide (CFG) in the CCHS sample, was explored. Over-consumption of Meat and Alternatives was prevalent, and positively associated with obesity. The second and third studies examined the immediate impact of the Healthy Eating Initiative (HEI). An online survey was developed to explore employees' dietary patterns at work and at home. The survey did not find evidence that employees were changing their consumption of non-recommended food at work and at home. In addition, employees who ate healthily at work also ate healthily at home. The third study used cafeteria sales data to explore the impact of HEI on employees' food purchases, especially on those items that were targeted by the Initiative. The results showed that cafeteria sales revenue did not decrease, and that sales of targeted healthy food items increased during the HEI period, after controlling for price and time. The last study modelled the effect of healthy eating in the long run using decision analysis incorporating data from the literature and the aforementioned three studies. The baseline Incremental Cost Utility Ratio (ICUR) was $9,360 per Quality Adjusted Life Years (QAL Y), but was very sensitive to changes in certain parameters, especially those that affected QALY, sucli as the relative risk (RR) of weight gain from healthy eating. This thesis provided some evidence about the impact of the HEI, and identified issues in the evaluation of such interventions that could be used to develop stronger evaluations in future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".