Evidence map›Paper›PMID 38269193›Full record

ArticleJournal of Great Lakes research2022

Community engagement and the importance of partnerships within the Great Lakes Areas of Concern program: A mixed-methods case study.

Alison Rentschler, Kathleen C Williams

Abstract read
In one paragraph

Article in Journal of Great Lakes research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Alison RentschlerUniversity of Michigan, 440 Church Street, Ann Arbor, MI 48109, USA.
Kathleen C WilliamsUnited States Environmental Protection Agency Great Lakes Toxicology and Ecology Division, 6201 Congdon Blvd, Duluth, MN 55804, USA.

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

The Great Lakes Areas of Concern (AOC) program was created through amendments to the Great Lakes Water Quality Agreement (GLWQA) in 1987 to restore contaminated sites using an ecosystem-based approach. This program represents one of the first instances of ecosystem-based management (EBM) in the Great Lakes region with a specific focus on the inclusion of the public and local stakeholders in the process. Despite official language incorporating EBM in the AOC program, implementation of these practices has not been consistent across AOCs given differences in local arrangements of Public Advisory Councils (PACs), approaches to community engagement, and environmental problems. To better understand community engagement in these complex AOCs, this research investigated community, PAC, and state agency perspectives in three AOCs in Michigan: the Kalamazoo River, Saginaw River and Bay, and Rouge River AOCs. We gathered data through interviews, focus groups, and participatory observations with community members, PAC members, and state officials in each AOC. Findings indicate that communities in these areas have minimal connection to the AOC program and PACs. Community members tended to have greater connection to local organizations that provide a variety of opportunities for community members to engage with their environment in ways they value. To better connect the public to the AOC program, PACs may benefit from intentional partnerships with community organizations to increase community engagement. To consistently bolster community engagement in AOCs, we further recommend that state agencies provide additional resources to improve connection to local communities.

Indexed as

Areas of Concern programCommunity engagementPartnerships

Identifiers

PMID38269193
PMCPMC10807300

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.