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Record W3106911290 · doi:10.1016/j.envint.2020.106275

Review of the evidence for oceans and human health relationships in Europe: A systematic map

2020· review· en· W3106911290 on OpenAlexaboutno aff
Rebecca Short, Daniel T. C. Cox, Yin Ling Tan, Alison Bethel, Jacqualyn Eales, Ruth Garside

Bibliographic record

VenueEnvironment International · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
FundersHorizon 2020Horizon 2020 Framework Programme
KeywordsStakeholderGrey literatureSystematic reviewPublic healthHuman healthInclusion (mineral)Political scienceGeographyEnvironmental resource managementMEDLINEEnvironmental healthPublic relationsMedicineSociologySocial scienceEnvironmental science

Abstract

fetched live from OpenAlex

BACKGROUND: Globally, there is increasing scientific evidence of critical links between the oceans and human health, with research into issues such as pollution, harmful algal blooms and nutritional contributions. However, Oceans and Human Health (OHH) remains an emerging discipline. As such these links are poorly recognized in policy efforts such as the Sustainable Development Goals, with OHH not included in either marine (SDG14) or health (SDG3) goals. This is arguably short-sighted given recent development strategies such as the EU Blue Growth Agenda. OBJECTIVES: In this systematic map we aim to build on recent efforts to enhance OHH in Europe by setting a baseline of existing evidence, asking: What links have been researched between marine environments and the positive and negative impacts to human health and wellbeing? METHODS: We searched eight bibliographic databases and queried 57 organizations identified through stakeholder consultation. Results include primary research and systematic reviews which were screened double blind against pre-defined inclusion criteria as per a published protocol. Studies were limited to Europe, US, Australia, New Zealand and Canada. Data was extracted according to a stakeholder-defined code book. A narrative synthesis explores the current evidence for relationships between marine exposures and human health outcomes, trends in knowledge gaps and change over time in the OHH research landscape. The resulting database is available on the website of the Seas, Oceans and Public Health in Europe website (https://sophie2020.eu/). RESULTS: A total of 1,542 unique articles were included in the database, including those examined within 56 systematic reviews. Research was dominated by a US focus representing 50.1% of articles. A high number of articles were found to link: marine biotechnology and cardiovascular or immune conditions, consumption of seafood and cardiovascular health, chemical pollution and neurological conditions, microbial pollution and gastrointestinal or respiratory health, and oil industry occupations with mental health. A lack of evidence relates to direct impacts of plastic pollution and work within a number of industries identified as relevant by stakeholders. Research over time is dominated by marine biotechnology, though this is narrow in focus. Pollution, food and disease/injury research follow similar trajectories. Wellbeing and climate change have emerged more recently as key topics but lag behind other categories in volume of evidence. CONCLUSIONS: The evidence base for OHH of relevance to European policy is growing but remains patchy and poorly co-ordinated. Considerable scope for future evidence synthesis exists to better inform policy-makers, though reviews need to better incorporate complex exposures. Priorities for future research include: proactive assessments of chemical pollutants, measurable impacts arising from climate change, effects of emerging marine industries, and regional and global assessments for OHH interactions. Understanding of synergistic effects across multiple exposures and outcomes using systems approaches is recommended to guide policies within the Blue Growth Strategy. Co-ordination of research across Europe and dedicated centres of research would be effective first steps.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.777
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.341
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations30
Published2020
Admission routes1
Has abstractyes

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