A qualitative study of the drivers of socioeconomic inequalities in men’s eating behaviours
Bibliographic record
Abstract
BACKGROUND: Men of low socioeconomic position (SEP) are less likely than those of higher SEP to consume fruits and vegetables, and more likely to eat processed discretionary foods. Education level is a widely used marker of SEP. Few studies have explored determinants of socioeconomic inequalities in men's eating behaviours. The present study aimed to explore intrapersonal, social and environmental factors potentially contributing to educational inequalities in men's eating behaviour. METHODS: Thirty Australian men aged 18-60 years (15 each with tertiary or non-tertiary education) from two large metropolitan sites (Melbourne, Victoria; and Newcastle, New South Wales) participated in qualitative, semi-structured, one-on-one telephone interviews about their perceptions of influences on their and other men's eating behaviours. The social ecological model informed interview question development, and data were examined using abductive thematic analysis. RESULTS: Themes equally salient across tertiary and non-tertiary educated groups included attitudes about masculinity; nutrition knowledge and awareness; 'moralising' consumption of certain foods; the influence of children on eating; availability of healthy foods; convenience; and the interplay between cost, convenience, taste and healthfulness when choosing foods. More prominent influences among tertiary educated men included using advanced cooking skills but having relatively infrequent involvement in other food-related tasks; the influence of partner/spouse support on eating; access to healthy food; and cost. More predominant influences among non-tertiary educated men included having fewer cooking skills but frequent involvement in food-related tasks; identifying that 'no-one' influenced their diet; having mobile worksites; and adhering to food budgets. CONCLUSIONS: This study identified key similarities and differences in perceived influences on eating behaviours among men with lower and higher education levels. Further research is needed to determine the extent to which such influences explain socioeconomic variations in men's dietary intakes, and to identify feasible strategies that might support healthy eating among men in different 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".