Suicide and the COVID-19 pandemic: A qualitative study of discourse on an online pro-choice for suicide discussion forum
Bibliographic record
Abstract
The COVID-19 pandemic had a widespread impact on millions of individuals. Many turned to social media as an outlet for sharing personal experiences, such as the impact of the pandemic on suicidality. The purpose of this study was to understand the pandemic’s impact on individuals who discuss their suicidality on social media. Keywords were used to search for discussion threads (N = 118) related to the pandemic on an online pro-choice for suicide forum. Using reflexive thematic analysis, six themes related to the pandemic’s impact on mental health, suicidality, living conditions, and optimism were identified. Examination of the content from pro-choice for suicide forums may yield authentic information on the impact of the pandemic on those considering suicide. This study contributes to our understanding of the nuances of factors impacting mental health and suicidality during the pandemic, including unique risk and protective factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".