Binary gender differences in the domains of body regard and nonsuicidal self-injury
Bibliographic record
Abstract
There is growing evidence of the relevance of body regard (i.e., one's relationship with, attitude toward, and experience of the body) to nonsuicidal self-injury (NSSI) among college students, although there remain notable gaps in our understanding of this relationship. The present study thus explored (1) binary gender differences in body regard subdomains (i.e., body acceptance, athleticism, body care, body connection) among college students reporting past-year NSSI and (2) the relative contribution of each subdomain in predicting past-year NSSI across binary genders. College students ( N = 3343; 12.7 % of whom reported past-year NSSI) completed online measures of NSSI and body regard. Results revealed that women reported lower body acceptance and athleticism, but similar body care and connection, relative to men. Among women, all subdomains of body regard were significant negative predictors of past-year NSSI, whereas among men, only body care and connection were. Findings lend support for the protective role of high body regard in relation to NSSI among college students and advance our understanding of binary gender differences in this relationship. • Women report lower levels of body acceptance and athleticism than men • Body acceptance and athleticism predict less past-year self-injury in women only • Women and men report similar levels of body care and body connection • Body care and body connection predict less past-year self-injury in women and men
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".