Evaluation of an intervention limiting food industry influence on public food policy processes in Ghana
Bibliographic record
Abstract
This study evaluates the immediate effect of an educational intervention implemented among key policy actors in Ghana. The intervention focused on creating awareness and increasing competencies for countering food industry public food policy dilution strategies. The intervention was evaluated using a before-and-after design, collecting self-reported awareness, appropriateness, competencies, and skill level rating, and using frequencies, percentages, and non-parametric testing (Wilcoxon rank-sum test, with alpha set at 0.05) to report results. Thirty policy actors attended the workshop, but 23 and 17 participated in the evaluation (pre- and post-workshop, respectively). Most (82%) were health experts, with about 48% reporting two decades or more of professional experience. Before the intervention, policy actors reported receiving job offers, promotional material, and sponsored travel from the food industry. After the workshop, policy actors' overall mean appropriateness level rating of such strategies decreased (from 2.60 ± 0.87 to 1.95 ± 0.81; P = 0.013). Policy actors' overall awareness level rating of food industry using such strategies to influence public food policies increased after the workshop (from 4.27 ± 0.55 to 4.38 ± 0.59; P = 0.657). Similarly, their overall mean competencies and skill level rating for recognizing and countering public food policy dilution strategies increased (from 2.70 ± 0.54 to 3.13 ± 0.41; P = 0.012). The findings show the potential of an educational workshop serving as a preemptive intervention to protect public food policies from industry influence, and for such interventions to be incorporated into national food policy development processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".