Old Bonds Never Break: A Scoping Review of Older LGBTQ+ Adults’ Use of Online Technologies for Social Connections During the COVID-19 Pandemic
Bibliographic record
Abstract
Online technologies and digital platforms like social media, dating apps, and other sites are popular among people who identify as lesbian, gay, bisexual, transgender, and other gender and sexual minorities (LGBTQ+).During the COVID-19 pandemic, people-especially older adults-relied heavily on these technologies to maintain personal and social connections because of lockdown measures and risks of viral transmission.The purpose of this study was to investigate older LGBTQ+ adults' experiences with online technologies to form and maintain relationships during the COVID-19 pandemic.Following Arksey and O'Malley's framework for scoping reviews, we searched seven databases and screened potential studies, which yielded seven studies for inclusion.Data was extracted and thematically analyzed, resulting in five themes related to how older LGBTQ+ people used online technologies to maintain personal connections during the pandemic and the benefits of and barriers to incorporating such technologies.Notably, we found that older LGBTQ+ adults were resilient, being willing and able to adapt to sociotechnical challenges during the pandemic.Older LGBTQ+ adults felt an obligation to support other members of the LGBTQ+ community, and feelings of responsibility and solidarity motivated older LGBTQ+ adults to incorporate online technologies into their social practices to care for one another.This scoping review provides directions for future research and offers recommendations for how policymakers, community organizations, and other stakeholders can better serve the technological and social health needs of older LGBTQ+ adults in our post-pandemic world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".