Navigating Gender Dynamics and Identity Formation in Online Mutual Support Communities: Insights from Participant Experiences
Bibliographic record
Abstract
The COVID-19 pandemic has driven individuals to seek support through online mutual support groups, which provide spaces for knowledge exchange, social support, and crisis management. While these groups are known to benefit mental health, little is understood about how gender shapes interactions and identity formation within them. This study explores gender dynamics, communication styles, and identity formation in online support groups during the pandemic, focusing on their role in challenging traditional gender norms and fostering inclusivity. Two focus groups and three semi-structured interviews were conducted with 13 Chinese adults (9 female, 4 male) aged 24 to 38 in the U.S. and Canada, all with graduate degrees. Participants, recruited via flyers and the author’s network, shared their experiences using an online support group from spring 2022 to spring 2023. Data were analyzed thematically using Atlas.ti. Findings reveal female participants emphasized solidarity, empowerment, and addressing gender roles, often uniting against issues like sexual harassment. Male participants showed varying support for gender equality, with some hesitant to engage in emotionally charged discussions. The study highlights the potential of online groups to promote gender equality and solidarity during crises, offering insights for inclusive support spaces.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".