Connectedness in the time of COVID-19: Reddit as a source of support for coping with suicidal thinking
Bibliographic record
Abstract
The COVID-19 pandemic is adversely impacting suicidality at a population level, with consequences resulting from a variety of pandemic-driven disruptions, including social activities and connectedness. This paper uses a single case study design to explore how members of the Reddit r/COVID19_support community create a sense of connectedness among those who have suicidal thoughts due to the pandemic. Data were gathered from posts to the r/COVID19_support subreddit forum from February 2020 through December 2020. The second step of Klonsky and May's (2015) Three-Step Theory (3ST) of suicide, connectedness as a key protective factor, was used as the theoretical framework. This study explored r/COVID19_support's constructed environment, users' dialogical interactions, and the four primary tenets of connectedness as proposed by Klonsky and May – Purpose and Meaning, Relationships, Religiosity, and Employment. Findings demonstrate a deep sense of connectedness for online community members. Relationships and Purpose and Meaning featured as the most salient sources of connectedness within this subreddit, whereas Religiosity was rarely discussed, and Employment was often spoken of in negative terms (i.e., creating mental distress, rather than facilitating connectedness). Contributors' responses offered various opportunities for connectedness both on- and off-line. Safe online spaces, such as r/COVID19_support, can serve as a protective factor amid suicidality, facilitating connectedness, and thereby helping to curtail suicidal thoughts from advancing to suicidal actions. This subreddit and similar online spaces can benefit specific populations who may otherwise find it challenging to access services or who wish to remain anonymous.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".