Ensuring equitable, inclusive and meaningful gender identity- and sexual orientation-related data collection in the healthcare sector: insights from a critical, pragmatic systematic review of the literature.
Saved in:
| Title: | Ensuring equitable, inclusive and meaningful gender identity- and sexual orientation-related data collection in the healthcare sector: insights from a critical, pragmatic systematic review of the literature. |
|---|---|
| Authors: | Bragazzi, Nicola Luigi, Khamisy-Farah, Rola, Converti, Manlio |
| Source: | International Review of Psychiatry. May/Jun2022, Vol. 34 Issue 3/4, p282-291. 10p. |
| Subjects: | Sexual orientation, Health care industry, Online information services, Systematic reviews, Self-evaluation, Natural language processing, Human sexuality, Acquisition of data, Artificial intelligence, Gender identity, LGBTQ+ people, Sex customs, Sexual orientation identity, Sociodemographic factors, MEDLINE, Respect, Social integration, Data mining |
| Abstract: | In several countries, no gender identity- and sexual orientation-related data is routinely collected, if not for specific health or administrative/social purposes. Implementing and ensuring equitable and inclusive socio-demographic data collection is of paramount importance, given that the LGBTI community suffers from a disproportionate burden in terms of both communicable and non-communicable diseases. To the best of the authors' knowledge, there exists no systematic review addressing the methods that can be implemented in capturing gender identity- and sexual orientation-related data in the healthcare sector. A systematic literature review was conducted for filling in this gap of knowledge. Twenty-three articles were retained and analysed: two focussed on self-reported data, two on structured/semi-structured data, seven on text-mining, natural language processing, and other emerging artificial intelligence-based techniques, two on challenges in capturing sexual and gender-diverse populations, eight on the willingness to disclose gender identity and sexual orientation, and, finally, two on integrating structured and unstructured data. Our systematic literature review found that, despite the importance of collecting gender identity- and sexual orientation-related data and its increasing societal acceptance from the LGBTI community, several issues have to be addressed yet. Transgender, non-binary identities, and also intersex individuals remain often invisible and marginalized. In the last decades, there has been an increasing adoption of structured data. However, exploiting unstructured data seems to overperform in identifying LGBTI members, especially integrating structured and unstructured data. Self-declared/self-perceived/self-disclosed definitions, while being respectful of one's perception, may not completely be aligned with sexual behaviours and activities. Incorporating different levels of information (biological, socio-demographic, behavioural, and clinical) would enable overcoming this pitfall. A shift from a rigid/static nomenclature towards a more nuanced, dynamic, 'fuzzy' concept of a 'computable phenotype' has been proposed in the literature to capture the complexity of sexual identities and trajectories. On the other hand, excessive fragmentation has to be avoided considering that: (i) a full list of options including all gender identities and sexual orientations will never be available; (ii) these options should be easily understood by the general population, and (iii) these options should be consistent in such a way that can be compared among various studies and surveys. Only in this way, data collection can be clinically meaningful: that is to say, to impact clinical outcomes at the individual and population level, and to promote further research in the field. [ABSTRACT FROM AUTHOR] |
| Copyright of International Review of Psychiatry is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
