The Enhanced Critical Incident Technique Investigation of Girls’ Perceptions of Prosocial Connectedness in a Wraparound Program
Bibliographic record
Abstract
Women and girls are being implicated in gang-related operations at alarming rates. Anti-social gang behaviours such as drug trafficking, sexual exploitation, gun violence, and street entrenchment are of particular concern. British Columbia has seen a rise in gang-associated violence and homicide directed at or involving women over the last decade. Positive youth development initiatives such as the one in this study aim to support youth currently involved in or at risk of being involved in gangs. School personnel identify students who are exposed to anti-social gang behaviours and refer them to a wraparound program where they are matched with an adult mentor who works with them and their families to facilitate prosocial connections to five life domains: (a) school, (b) community, (c) home, (d) prosocial peers, and (e) the self. A 2012 evaluation report determined the program to be effective in reaching its objectives with a predominantly male population (84%). However, between 2015 and 2016, the program dramatically increased its responsiveness to girls, with a nearly 50% increase in female referrals. Using the enhanced critical incident technique (ECIT), the purpose of the study was to describe how female-identifying students articulate “prosocial connectedness” within the context of their experiences in a school-based wraparound gang prevention program. Critical incidents were collected by the first author, who interviewed eight girls and asked them the following: “What has helped/hindered/would have better helped facilitate your prosocial connectedness?” Findings were organized into 34 categories. ECIT analyses point to the effectiveness of using a relational/attachment model to inform strategies for gang prevention and school-based intervention in female youth.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".