<b>A critical exploration of stigma interventions and programs for queer peoples across the life course in Canada: a scoping review</b> <b>protocol</b>
Bibliographic record
Abstract
Objective: This scoping review aims to critically explore the current state of interventions and programs designed to reduce queer-related stigma across the life course in Canada, with an emphasis on understanding their impact, successes, and gaps.Introduction: Queer populations (sexual and gender diverse people, including two-spirit, lesbian, gay, bisexual, trans, and queer) in Canada face significant health disparities, largely driven by stigma related to sexual and gender identities. These inequities are associated with higher rates of mental health issues, substance abuse, suicidality, and other adverse outcomes. Although a growing body of literature examines stigma reduction, interventions often focus on specific types of queer stigma, such as sexual minority stigma or HIV stigma, rather than considering the broader, intersectional experiences of queer people. This review will map the landscape of stigma-reduction interventions for queer populations across Canada, aiming to identify key trends, effectiveness, and areas for improvement.Inclusion Criteria: This review will include studies that address interventions aimed at reducing queer-related stigma in Canadian contexts. Eligible studies must focus on queer populations, including individuals from diverse sexual and gender identities, and measure outcomes related to stigma reduction. Studies of any design (qualitative, quantitative, mixed methods), published in English or French, and from any year will be considered.Methods: A systematic search will be conducted across multiple databases, including Medline-R (Ovid), Embase (Ovid), APA PsycInfo (Ovid), CINAHL (EBSCO), Social Services Abstracts (ProQuest), Social Science Abstracts (ProQuest), and Social Science Citation Index (Web of Science), along with grey literature sources. Two reviewers will independently assess potential articles against the inclusion criteria through two stages: titles and abstracts as well as full-text screening. Data extraction will focus on study characteristics, intervention details, and outcomes related to stigma reduction. Findings will be summarized in tables and narrative summaries, guided by the socioecological model and intersectional queer theories. Data will be analyzed to identify trends and gaps in current interventions aimed at addressing queer stigma.
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 | 0.039 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".