Suicidality and protective factors among sexual and gender minority youth and adults in Canada: a cross-sectional, population-based study
Bibliographic record
Abstract
BACKGROUND: Sexual and gender minority populations experience elevated risks for suicidality. This study aimed to assess prevalence and disparities in non-fatal suicidality and potential protective factors related to social support and health care access among sexual and gender minority youth and adults and their heterosexual and cisgender counterparts in Canada. The second objective was to examine changes in the prevalence of suicidal ideation and protective factors during the COVID-19 pandemic. METHODS: Pooled data from the 2015, 2016 and 2019 Canadian Community Health Surveys were used to estimate pre-pandemic prevalence of suicidal ideation, plans and attempts, and protective factors. The study also estimated changes in the prevalence of recent suicidal ideation and protective factors in fall 2020, compared with the same period pre-pandemic. RESULTS: The prevalence of suicidality was higher among the sexual minority populations compared with the heterosexual population, and the prevalence was highest among the bisexual population, regardless of sex or age group. The pre-pandemic prevalence of recent suicidal ideation was 14.0% for the bisexual population, 5.2% for the gay/lesbian population, and 2.4% for the heterosexual population. The prevalence of lifetime suicide attempts was 16.6%, 8.6%, and 2.8% respectively. More than 40% of sexual minority populations aged 15-44 years had lifetime suicidal ideation; 64.3% and 36.5% of the gender minority population had lifetime suicidal ideation and suicide attempts. Sexual and gender minority populations had a lower prevalence of protective factors related to social support and health care access. The prevalence of recent suicidal ideation among sexual and gender minority populations increased in fall 2020, and they tended to experience longer wait times for immediate care needed. CONCLUSIONS: Sexual and gender minority populations had a higher prevalence of suicidality and less social support and health care access compared to the heterosexual and cisgender populations. The pandemic was associated with increased suicidal ideation and limited access to care for these groups. Public health interventions that target modifiable protective factors may help decrease suicidality and reduce health disparities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".