Health Promotion Values Underlying Healthy Eating Strategies in The Netherlands
Bibliographic record
Abstract
Healthy eating strategies are a large focus of research, practice, and policy in the Netherlands to improve the diets of socioeconomically disadvantaged populations (SDPs) and reduce health inequalities. However, the fundamental values of the health professionals that develop, implement, and evaluate healthy eating strategies are not explicit. Understanding and challenging these values may be an important step in aligning and improving efforts to support healthy diets in SDPs. The purpose of this qualitative study was to critically examine the values influencing strategies to promote healthy eating in SDPs in the Netherlands. In-depth interviews guided by a critical health promotion model were conducted with a diverse group of health professionals (n = 29) between October 2020 and January 2021 and analyzed using reflective thematic analysis. Results indicated that health professionals’ values overlapped in many ways, including their shared values concerning beneficence, responsibility, and collaboration. However, value conflicts were also uncovered surrounding assumptions about SDPs and ethical change processes. The co-existence of conventional and holistic health promotion values also reflected an enduring emphasis on individual-level healthy eating strategies. It is concluded that ongoing attention to the values of health professionals is needed to advance healthy eating strategies and reduce diet-related health inequalities.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".