Bibliographic record
Abstract
Research with 2SLGBTQIA+ communities is a growing domain of interest in Canada (MHCC, 2012), yet, still considered inadequate (Waite & Denier, 2019). Research on mental health, for instance, is robust in the description of the characteristics of mental health across these populations (Filice & Meyer, 2018; Kidd et al., 2016; McIntyre et al., 2011; Veltman & Chaimowitz, 2014); however, there are insufficient initiatives investigating the kinds of care and treatment that would best serve these communities (Pachankis et al., 2022). 2SLGBTQIA+ populations are known to be negatively affected by stigma and discrimination, which impact mental health outcomes and treatment access (Filice & Meyer, 2018; Kidd et al., 2016; McIntyre et al., 2011; Veltman & Chaimowitz, 2014). Thus, it is vital to understand how these communities experience oppression and find ways to buffer against the perils of prejudice. The pandemic is an exceptional circumstance that offers insight to exceedingly difficult conditions, which can shed light on exacerbated inequities, as well as protective coping strategies. Moreover, the pandemic impacted delivery of mental health care, with many practitioners transitioning to remote service provision. Mental health care providers working with 2SLGBTQIA+ populations during the pandemic are in an advantageous position to provide illuminating information on service provision, as well as the challenges and wellbeing of 2SLGBTQIA+ populations.This study focuses on the experiences of mental health care service providers who worked with 2SLGBTQIA+ communities in Montreal, Canada during the first year of the Coronavirus disease pandemic. Using the Integrated Social Justice Consultation Model (Sinacore, 2022) as an epistemological framework, this qualitative inquiry study employed interpretive phenomenology to explore clinician experiences. Participants were 14 members of a network of clinicians dedicated to providing affirmative mental health care for sexual and gender minorities. Data was collected via demographic survey, three focus groups, and 14 individual interviews. Two major domains emerged from the results: the evolution of service provision in response to the pandemic, and subsequent changes in therapeutic processes. These domains offer insight on remote therapy, approaches to practice, new clinical experiences that arose during the pandemic, clinician knowledge mobilization, clinician resource sharing, and how 2SLGBTQIA+ clients were impacted during the pandemic
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".