Examining Nock and Prinstein’s four-function model with offenders who self-injure.
Bibliographic record
Abstract
Nonsuicidal self-injury (NSSI) is the deliberate bodily harm or disfigurement without suicidal intent and for purposes not socially sanctioned (e.g., cutting, burning, head banging). Nock and Prinstein (2004) proposed a 4-function model (FFM) of NSSI, in which the functions of NSSI are categorized by two dichotomous factors: (a) positive (i.e., involves the addition of a favorable stimulus) or negative (i.e., involves the removal of an aversive stimulus; and (b) automatic (i.e., intrapersonal) or social (i.e., interpersonal). This study examined the validity of this model with incarcerated populations. In-depth semistructured interviews with 201 incarcerated offenders were analyzed and categorized based on the FFM. Participants' descriptions of functions of NSSI were most commonly categorized as automatic negative reinforcement (25.0%; e.g., coping with negative emotions), followed by automatic positive reinforcement (31.3%; e.g., self-punishment), social positive reinforcement (31.3%; e.g., to communicate with others), and social negative reinforcement (12.5%; e.g., to avoid hurting someone else). While the uniqueness of the correctional environment affects some of the specific functions evident in offenders, FFM can be used to adequately organize the functions of NSSI in offenders, providing a useful tool for explaining this complex behavior. Clinically, NSSI in offenders can be viewed has having the same underlying motivations, although automatic positive reinforcement is more prevalent in offenders and social positive reinforcement is more prevalence in nonoffenders. Given that the motivations underlying nonsuicidal self-injury are similar for offender and nonoffender populations, similar treatment approaches may be effective with both populations. (PsycINFO Database Record
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".