DOI: 10.3886/e146781v2-186789
title: Dataset for "Community-Academic Partnerships in the Community Engagement Literature: A Scoping Review"

description: We contribute a novel dataset mapping the community-university partnership journal articles in the community engagement field. Files include:<br><b>flow chart </b>of the article selection process, <b>code book</b> developed for the categories and characteristics analyzed,<b>article level results</b>, or way in which each article was coded according to the categories and characteristics used in the scoping review, and <b>total counts</b> of the categories and characteristics review across articles.This scoping review includes key article-level characteristics regarding the representation of community-academic partnerships within 141 published articles from seven peer-reviewed journals in the community engagement field from 1994-2020.
description: other; <b>Identification<br></b>We used a three-step search strategy as recommended in all JBI (2015) types of reviews. The first step is an initial limited search of at least four online databases relevant to the topic. Our initial search was conducted in Education Source, ProQuest, Directory of Open Access Journals, and Academic Search Complete.<br><br>Our search using the root Boolean terms relat* and partner* yielded the greatest number of relevant and fewest number of irrelevant articles (we had also tried relat* OR partner* to less success and efficiency). We limited the search to the abstracts rather than full text. However, because the search platform used for the Michigan Journal of Community Service Learning does not allow one to search abstracts according to our protocol, we used a collated set of abstracts sent by the journal editor (upon request) and searched that document. Our rationale was that if an article is discussing a community-academic partnership, then it would be difficult to convey the focus of that topic in the abstract without also using these root terms. In our search, we included all articles written from the start of the publication until May 2020. Searching the seven journals only, we identified 1043 articles for review, after removing 48 articles that were duplicates, book reviews, or letters to the editor. For additional information about our methods, please contact the lead author.<br><br><b>Screening<br></b>The next step was to screen the 995 articles based on a full abstract review. Two reviewers read through all article abstracts to determine whether they met our goal: Does this article tell us something about community-academic partnerships? Because we were interested in how partnerships work, we were also interested in only those articles that used the partnership as an area of focus or a unit of analysis. Questions that guided this process were: Is there a description of who was involved in the partnership? Is there a description of processes or the results of the partnership work? Based on this manual review of the abstracts, we excluded a further 813.<br><b><br>Eligibility</b>The final step examined the remaining 182 articles in a full text review. Two authors conducted an initial review of the full text articles and identified 41 that did not meet our initial criteria. There was concordance during this process. Articles that received split decisions or that met only some of the inclusion criteria were discussed to reach consensus with the third reviewer. This process resulted in 141 articles included in this study. See the appendix for the 141 articles included, with their assigned characteristics.<br><b><br>Data Charting</b>           <br>The data extraction process, also referred to as “data charting,” was developed and iteratively refined during the full text reviews of the 182 manuscripts by two reviewers and finalized with the inclusion of a third reviewer. They were informed by the types of information one might need to map the literature with regards to the scope of scholarship and the types of evidence used by scholars, as stated in our purpose. We read articles and charted data along eight categories. The authors discussed any discrepancies until they reached consensus. This helped to clarify and refine the definition and description of the categories and characteristics in the Codebook.