Examining the Impact of a Public Health Message on Fish Consumption in Bermuda
Bibliographic record
Abstract
BACKGROUND: In 2003 mean cord blood mercury concentrations in pregnant Bermudian women exceeded levels associated with adverse health outcomes in children. The principal mercury source was local fish species. Public health messages were developed suggesting pregnant women reduce consumption of fish species with higher mercury concentrations (e.g. swordfish), substituting species containing lower mercury concentrations, and elevated omega-3 fatty acids (e.g. anchovies). Recent evidence indicates mercury concentrations in Bermuda's pregnant women have fallen five- fold. OBJECTIVES: Assess whether changes in women's fish eating patterns during pregnancy are consistent with the public health messaging. Determine who is making changes to their diet during pregnancy and why. METHODS: Mixed methods study with a cross-sectional survey of 121 pregnant women, including 13 opened-ended interviews. Health system, social vulnerability, public health messaging, and socio-demographic variables were characterized and related to changes in fish consumption during pregnancy. Qualitative data were coded according to nutritional advice messages, comprehension of communication strategies, and sources of information. RESULTS: 95% of women surveyed encountered recommendations about fish consumption during pregnancy. 75% reported modifying fish eating behaviors because of recommendations. Principal sources of information about fish consumption in pregnancy were health care providers and the Internet. 71% of women reported reducing consumption of large fish species with greater mercury levels, but 60% reported reduced consumption of smaller, low mercury fish. No participant mentioned hearing about the benefits of fish consumption. More frequent exposure to public health messages during pregnancy was associated with lower reported consumption. Bermudian born women were less likely to reduce consumption of large fish species during pregnancy. CONCLUSIONS: In Bermuda, public health messages advocating reduced consumption of larger, higher mercury-containing fish species appear effective, but masked the nutritional value message of small fish species, with low mercury concentration. Adjustment is needed to better balance the risk communication.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".