Bibliographic record
Abstract
Abstract. Background: Although a wide range of studies discuss prevalence and risk factors associated with self-harm, protective factors that are equally important are rarely explored. Moreover, much of our understanding of young individuals who engage in self-harm come from studies conducted in Western countries with very little emphasis on marginalized groups. Aim: This scoping review identifies research on resilience among marginalized youth and youth living in low- and middle-income countries (LMICs) who show evidence of self-harm. Method: A scoping review following Arksey and O’Malley’s (2005) framework was conducted. This effort included drawing upon peer-reviewed research published between January 2000 and September 2020 to identify protective factors and coping strategies that are employed by individuals 10–29 years old with self-harming tendencies. Results: A total of 15 original papers met the inclusion criteria. The majority of the LMIC publications were from China. Social support, positive youth development, and religiosity were the most frequently reported protective factors. Conclusion: Despite widespread concern about self-harm, there are few peer-reviewed articles that look at resilience or recovery among youth in LMICs and among marginalized young people. In addition to various internal and external protective factors, this scoping review identifies gaps in our understanding of resilience to self-harm among youth belonging to these groups.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".