‘It’s not as good as the face‐to‐face contact’: A sociomaterialist analysis of the use of virtual care among Canadian gay, bisexual and queer men during the COVID‐19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic led to the widespread adoption of virtual care-the use of communication technologies to receive health care at home. We explored the differential impacts of the rapid transition to virtual care during the COVID-19 pandemic on health-care access and delivery for gay, bisexual and queer men (GBQM), a population that disproportionately experiences sexual and mental health disparities in Canada. Adopting a sociomaterial theoretical perspective, we analysed 93 semi-structured interviews with GBQM (n = 93) in Montreal, Toronto and Vancouver, Canada, conducted between November 2020 and February 2021 (n = 42) and June-October 2021 (n = 51). We focused on explicating how the dynamic relations of humans and non-humans in everyday virtual care practices have opened or foreclosed different care capacities for GBQM. Our analysis revealed that the rapid expansion and implementation of virtual care during the COVID-19 pandemic enacted disruptions and challenges while providing benefits to health-care access among some GBQM. Further, virtual care required participants to change their sociomaterial practices to receive health care effectively, including learning new ways of communicating with providers. Our sociomaterial analysis provides a framework that helps identify what works and what needs to be improved when delivering virtual care to meet the health needs of GBQM and other diverse populations.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.035 | 0.040 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".