Measurement of nonsuicidal self injury in adolescents.
Bibliographic record
Abstract
The integration of standardized measures in the assessment of NSSI The potential role of standardized measures in treatment planning and practice The key components of a “gold standard” measurement tool The range and type and differences between standardized measures currently available The current evidence available on certain measures regarding reliability and validity How to contact those who have developed these measures of NSSIWith recent advances in the definition and understanding of the phenomenon of nonsuicidal self-injury (NSSI) in adolescents, new opportunities have emerged for assessing and monitoring adolescent NSSI in a systematic fashion. The use of well-designed measures has many potential benefits. For example, it is a systematic, objective, time efficient, often cost-effective, and sometimes norm-referenced means of gathering a considerable amount of relevant information about an individual. Although self-report measures do bring the risk of response biases (e.g., social desirability), such biases might be reduced with the use of• •• •••structured interviews, which also hold the advantage of being systematic and objective modes of gathering information. There are various reasons why mental health professionals might want to measure adolescent NSSI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".