An exploration of the social inequities underpinning nutritional intake in high risk communities
Bibliographic record
Abstract
The evidence is clear that poor fruit and vegetable consumption is linked with higher obesity rates. This doctoral project began with an aim to improve fruit and vegetable consumption in the researcher’s community. The Ipswich region has a low intake of fruit and vegetable consumption, high obesity rates and corresponding rates of non-communicable disease burden. An exploratory, mixed-methods research program, using a qualitatively driven, sequential research design, was chosen to develop a progressive, nuanced understanding of the problem within a social model of health. The findings of this doctoral project demonstrated that a clear understanding of socio-economic factors leading to food system insecurity is necessary before a community food strategy can be developed. In the first phase of this research, semi-structured interviews with key stakeholders within the Ipswich community were conducted, to explore their perceptions of the barriers and enablers of fruit and vegetable consumption in their community. Analysis of these interviews revealed participants were at the beginning of their journey in understanding these barriers and enablers in their region. This was followed by semi-structured interviews undertaken in the Toronto region (Canada), which is recognised as a world leader in implementing strategic initiatives to shape the nutritional intake within their community. These interviews revealed the strategic response undertaken in Toronto to address nutritional disparities, focused on addressing food system inequity. The second phase of this research aimed to understand if food insecurity risk factors, identified as a key issue in Toronto influencing nutritional intake, were also present in Ipswich. A detailed characterisation of the Ipswich population, analysing food insecurity risk factors through cross-sectional and longitudinal modelling was undertaken. Findings confirmed that the Ipswich community had a significant number of food insecurity risk factors. The outcomes of this study reinforce that a detailed analysis of a population must be undertaken to identify groups experiencing social inequity, so that social model health responses can be customised and prioritised to create an equitable food system. Current social health policy and associated initiatives in Ipswich do not currently achieve this.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".