Documenting research with transgender and gender diverse people: protocol for an evidence map and thematic analysis.

Syst Rev

Division of Community Health and Humanities, Faculty of Medicine, Memorial University, St. John's, Newfoundland and Labrador, Canada.

Published: February 2017

Background: There is limited information about how transgender, gender diverse, and Two-Spirit (trans) people have been represented and studied by researchers. The objectives of this study are to (1) map and describe trans research in the social sciences, sciences, humanities, health, education, and business, (2) identify evidence gaps and opportunities for more responsible research with trans people, (3) assess the use of text mining for study identification, and (4) increase access to trans research for key stakeholders through the creation of a web-based evidence map.

Methods: Study design was informed by community consultations and pilot searches. Eligibility criteria were established to include all original research of any design, including trans people or their health information, and published in English in peer-reviewed journals. A complex electronic search strategy based on relevant concepts in 15 databases was developed to obtain a broad range of results linked to transgender, gender diverse, and Two-Spirit individuals and communities. Searches conducted in early 2015 resulted in 25,242 references after removal of duplicates. Based on the number of references, resources, and an objective to capture upwards of 90% of the existing literature, this study is a good candidate for text mining using Latent Dirichlet Allocation to improve efficiency of the screening process. The following information will be collected for evidence mapping: study topic, study design, methods and data sources, recruitment strategies, sample size, sample demographics, researcher name and affiliation, country where research was conducted, funding source, and year of publication.

Discussion: The proposed research incorporates an extensive search strategy, text mining, and evidence map; it therefore has the potential to build on knowledge in several fields. Review results will increase awareness of existing trans research, identify evidence gaps, and inform strategic research prioritization. Publishing the map online will improve access to research for key stakeholders including community members, policy makers, and healthcare providers. This study will also contribute to knowledge in the area of text mining for study identification by providing an example of how semi-automation performs for screening on title and abstract and on full text.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319144PMC
http://dx.doi.org/10.1186/s13643-017-0427-5DOI Listing

Publication Analysis

Top Keywords

text mining
16
transgender gender diverse
12
trans people
12
evidence map
8
gender diverse two-spirit
8
study
8
identify evidence
8
evidence gaps
8
mining study
8
study identification
8

Similar Publications

Background: Fulminant type 1 diabetes mellitus (FT1DM) is a severe subtype of type 1 diabetes characterized by rapid onset, metabolic disturbances, and irreversible insulin secretion failure. Recent studies have suggested associations between FT1DM and certain medications, warranting further investigation.

Objectives: This study aims to analyze drugs associated with an increased risk of FT1DM using the Food and Drug Administration Adverse Event Reporting System (FAERS) database.

View Article and Find Full Text PDF

Anomalous Node Detection in Blockchain Networks Based on Graph Neural Networks.

Sensors (Basel)

December 2024

School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

With the rapid development of blockchain technology, fraudulent activities have significantly increased, posing a major threat to the personal assets of blockchain users. The blockchain transaction network formed during user transactions can be represented as a graph consisting of nodes and edges, making it suitable for a graph data structure. Fraudulent nodes in the transaction network are referred to as anomalous nodes.

View Article and Find Full Text PDF

Global Literature Analysis of Tumor Organoid and Tumor-on-Chip Research.

Cancers (Basel)

January 2025

Hybrid Technology Hub, Centre of Excellence, Institute of Basic Medical Sciences, University of Oslo, 0372 Oslo, Norway.

: Tumor organoid and tumor-on-chip (ToC) platforms replicate aspects of the anatomical and physiological states of tumors. They, therefore, serve as models for investigating tumor microenvironments, metastasis, and immune interactions, especially for precision drug testing. To map the changing research diversity and focus in this field, we performed a quality-controlled text analysis of categorized academic publications and clinical studies.

View Article and Find Full Text PDF

Melatonin is a hormone released by the pineal gland that regulates the sleep-wake cycle. It has been widely studied for its therapeutic effects on Alzheimer's disease (AD), particularly through the amyloidosis, oxidative stress, and neuroinflammation pathways. Nevertheless, the mechanisms through which it exerts its neuroprotective effects in AD are still largely unknown.

View Article and Find Full Text PDF

A Time Series Proposal Model to Define the Speed of Carbon Steel Corrosion in an Extreme Acid Environment.

Materials (Basel)

December 2024

Sustainable Mining Engineering Research Group, Department of Mining, Mechanic, Energetic and Construction Engineering, Higher Technical School of Engineering, University of Huelva, 21007 Huelva, Spain.

This article shows the behavior of the corrosive effect of acid mine water on carbon steel metal alloys. Mining equipment, composed of various steel alloys, is particularly prone to damage from highly acidic water. This corrosion results in material thinning, brittle fractures, fatigue cracks, and ultimately, equipment failure.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!