Fine-Grained Assessment of Nonsuicidal Self-Injury
Bibliographic record
Abstract
Abstract This chapter focuses on the use of fine-grained assessment approaches in nonsuicidal self-injury (NSSI) research. Fine-grained assessment has enabled researchers to collect near real-time information about the proximal precipitants, consequences, and correlates of NSSI; test theoretical models in ecologically valid contexts; and further our understanding of the temporal sequencing of NSSI-related events. The chapter begins by discussing the need to balance benefits against the potential costs in the context of choosing the frequency, timing, and duration of study assessments, as well as measurement strategies for NSSI and other variables. It then considers some of the unique ethical and technical challenges that are inherent within fine-grained assessment studies. The chapter also offers recommendations for maximizing participant compliance and retention, navigating technological challenges, limiting recruitment or selection biases, and implementing effective risk assessments. Finally, this chapter explores frontiers in the area of fine-grained NSSI assessment, providing recommendations for future work and summarizing potential clinical applications of these methods.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".