Every scar tells a story: Insight into people’s self-injury scar experiences
Bibliographic record
Abstract
Scarring, a common and salient consequence of non-suicidal self-injury (NSSI), remains an under-explored issue in the field. Thus, the current investigation explored NSSI scar perspectives using online testimony from members of a popular NSSI message board; focus was attenuated to a series of message board posts pertinent to people’s experiences with scars resulting from NSSI. Data (message board posts) were collected using the website’s search function. A total of 53 posts involving discussion of people’s NSSI scar perceptions and experiences were retained. A thematic analysis of the data indicated that individuals viewed their scars in a number of ways. Many viewed scars in a resilient manner, often in the context of a self-narrative. Others, however, were unaccepting of their scars (e.g. expressed feelings of shame, hatred, or disgust). For some, they were able to gradually accept their scars but only after a period of difficulty. And, finally, some individuals expressed mixed feelings toward their scars (e.g. a love/hate relationship). Hence, scars stemming from NSSI seem to differentially impact individuals who self-injure. Possible implications for research and clinical practice 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".