Promoting Healthy and Sustainable Diet: A blueprint from Cascais local government to empower the community
Bibliographic record
Abstract
ABSTRACT - Introduction: Considering Health Promotion an investment in sustainable socio-economic development, policies play a role both in facilitating and hindering intervention. Since diet is a modifiable risk factor with a high impact on the reduction of chronic non-communicable diseases, it is important to intervene in different environments and contexts, favouring a local approach. This study aims to identify and describe local dimensions related to healthy and sustainable eating, characterising the municipality of Cascais, for the future materialisation of a Local Plan for the Promotion of Healthy and Sustainable Eating. Methods: A mixed-methods approach was followed, based on the Dahlgren-Whitehead Model and the Ontario Health Promotion Planning Model, with data collected between November 2023 and June 2024. Primary/secondary and national/local data was collected, using quantitative analysis (primary data from the ‘Health and Well-being in Cascais’ questionnaire) and qualitative analysis (secondary data categorised with an ecological vision). Results/Discussion: When analysing the four dimensions used for the situational assessment (sociodemographic profile, health profile, community responses and policies/structures), Cascais faces inequalities at parish level, such as the disparity in purchasing power, the increase in vulnerable groups (e.g. immigrants), the ageing of the population or the existence of food insecurity. Despite these challenges, community responses, as well as the policies/structures created in the municipality, can contribute to improving socio-economic and health indicators, directly or indirectly related to food. Conclusions: As in the municipality of Cascais, local government occupies a key position in creating environments that promote health, support inclusion and foster participation, contributing to changes in social and individual behaviours related to eating habits. The use of an ecological model can facilitate aggregation and reflection on the multiplicity of levels that are involved in the complexity of the food system.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".