Understanding Canadian experiences of suicidality during the COVID-19 pandemic: protocol of a pan-Canadian qualitative study
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic led to major disruptions in society across many spheres, including healthcare, the economy and social behaviours. While early predictions warned of an increased risk of suicide during and after the COVID-19 pandemic, rates of suicide deaths remained stable or decreased over that period for most countries. In contrast, the prevalence of suicidal ideation doubled and suicide attempts slightly increased during the COVID-19 pandemic in the adult general population worldwide, accompanied by a higher prevalence of major depressive disorder and anxiety disorders. While these data can tell us what happened, they cannot tell us why. Qualitative suicide research seeks to understand experiences of individuals with suicide-related thoughts and behaviours, provides an in-depth exploration of their lives and interactions with others and centres their views and unique context. There is little qualitative research focusing on suicidality during the pandemic. This study will use a qualitative approach to explore the extent and impact of the COVID-19 pandemic on Canadians who experienced suicidality and review their experiences of accessing mental healthcare to identify key components in supporting safety and recovery. METHODS AND ANALYSIS: This study will involve approximately 100 semistructured interviews with participants across four Canadian provinces and will explore experiences with suicide-related thoughts and behaviours during the COVID-19 pandemic. Transcripts will be analysed through qualitative analysis informed by constructivist grounded theory. ETHICS AND DISSEMINATION: The study was approved by the Research Ethics Board of the Centre for Addiction and Mental Health, Toronto Academic Health Sciences Network (for JZ: CAMH REB No 104-2022). In addition to traditional peer-reviewed presentations and publications, a report will make study findings accessible to policy makers, media and the public.
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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 | 0.042 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".