Alberta’s Tomorrow Project: adherence to cancer prevention recommendations pertaining to diet, physical activity and body size
Bibliographic record
Abstract
OBJECTIVE: To explore cross-sectional adherence to cancer prevention recommendations by adults enrolled in a prospective cohort in Alberta, Canada. DESIGN: Questionnaire data were used to construct a composite cancer prevention adherence score for each participant, based on selected personal recommendations published by the World Cancer Research Fund/American Institute for Cancer Research (2007). Data were self-reported on health and lifestyle, past-year physical activity and past-year FFQ. The scores accounted for physical activity, dietary supplement use, body size, and intakes of alcohol, fruit, vegetables and red meat. Tobacco exposure was also included. Scores ranged from 0 (least adherent) to 7 (most adherent). SETTING: Alberta's Tomorrow Project; a research platform based on a prospective cohort. SUBJECTS: Adult men and women (n 24 988) aged 35-69 years recruited by random digit dialling and enrolled in Alberta's Tomorrow Project between 2001 and 2009. RESULTS: Of the cohort, 14 % achieved adherence scores ≥5 and 60 % had scores ≤3. Overall adherence scores were higher in women (mean (sd): 3·4 (1·1)) than in men (3·0 (1·2)). The extent of overall adherence was also associated with level of education, employment status, annual household income, personal history of chronic disease, family history of chronic disease and age. CONCLUSIONS: Reported adherence to selected personal recommendations for cancer prevention was low in this cohort of adults. In the short to medium term, these results suggest that more work is required to identify behaviours to target with cancer prevention strategies at a population level. Future work will explore the associations between adherence scores and cancer risk in this cohort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".