Advancing community-engaged research during the COVID-19 pandemic: Insights from a social network analysis of the trans-LINK Network
Bibliographic record
Abstract
Collaboration across sectors is critical to address complex health problems, particularly during the current COVID-19 pandemic. We examined the ability to collaborate during the pandemic as part of a baseline evaluation of an intersectoral network of healthcare and community organizations established to improve the collective response to transgender (trans) persons who have been sexually assaulted (the trans-LINK Network). A validated social network analysis survey was sent to 119 member organizations in Ontario, Canada. Survey respondents were asked, 'Has COVID-19 negatively affected your organization's ability to collaborate with other organizations on the support of trans survivors of sexual assault?' and 'How has COVID-19 negatively affected your organization's ability to collaborate within the trans-LINK Network?'. Data were analyzed using descriptive statistics. Seventy-eight member organizations participated in the survey (response rate = 66%). Most organizations (79%) indicated that the pandemic had affected their ability to collaborate with others in the network, citing most commonly, increased workload (77%), increased demand for services (57%), and technical and digital challenges (50%). Survey findings were shared in a stakeholder consultation with 22 representatives of 21 network member organizations. Stakeholders provided suggestions to prevent and address the challenges, barriers, and disruptions in serving trans survivors experienced during the pandemic, which were organized into themes. Seven themes were generated and used as a scaffold for the development of recommendations to advance the network, including: increase communication and knowledge exchange among member organizations through the establishment of a network discussion forum and capacity building group workshops; enhance awareness of network organizations by developing a member-facing directory of member services, their contributions, and ability to provide specific supports; strengthen capacity to provide virtual and in-person services and programs through enhanced IT support and increased opportunities for knowledge sharing and skill development; and adopt a network wide syndemic approach that addresses co-occurring epidemics (COVID-19 + racism, housing insecurity, transphobia, xenophobia) that impact trans survivors of sexual assault.
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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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".