Self-Injury During COVID-19
Bibliographic record
Abstract
ABSTRACT: Concerns have been raised about the impact of the COVID-19 pandemic on individuals with lived experience of nonsuicidal self-injury (NSSI). Yet, few efforts have explored this. Accordingly, using a mixed-methods approach, we sought to examine whether emerging adults who have self-injured experienced changes in NSSI urges and behavior during the pandemic and what may have accounted for these changes. To do so, university students with lived experience of NSSI completed online questions asking about NSSI and self-reported changes in urges and behavior since the onset of COVID-19. They then answered open-ended questions asking what contributed to these changes and how they have coped during this timeframe. Approximately 80% of participants reported no change or a decrease in NSSI urges and behavior. Participants discussed removal from stressors (e.g., social stress) that previously evoked NSSI, as well as having time for self-care and to develop resilience as accounting for this. Nevertheless, some participants reported challenges amid the pandemic (i.e., exacerbated stress, isolation); approximately one fifth of participants reported increases in NSSI urges and behavior. Our findings add to recent evidence that many individuals with prior mental health difficulties, including NSSI, can demonstrate resilience in the face of collective adversity. Research and clinician implications are discussed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".