Transforming injury prevention for youth (TrIPY): an intersectionality model for youth injury prevention
Bibliographic record
Abstract
Injury is deadly and expensive, and rates are increasing. The cost of injury is not only a financial burden; individuals, families and communities suffer the human costs of physical and emotional injury. For children and youth in Canada, injuries are the leading cause of death and disability. However, the risk of preventable injury is not equal for all youth. The transforming injury prevention for youth (TrIPY) model aims to recognise and remediate these inequities by applying an intersectionality lens to injury prevention programming. TrIPY conceptualises injury prevention programming through an intersectionality lens. The model was developed with diverse youth in mind, and the intended users include injury prevention practitioners, partners, stakeholders, communities and decision-makers. TrIPY was designed using a transformative perspective and built on core concepts within public health, injury prevention, intersectionality, gender analysis, youth risk, health equity, and systems of privilege and oppression. TrIPY helps to analyse intersecting inequities along multiple dimensions, to improve injury prevention programmes for diverse youth with unique identities, skills and lived experiences. The end goal of implementing an intersectionality model within injury prevention is to find out who is being missed in order to address existing inequities concerning youth injury. No matter what a person's unique social location or lived experience, they will have the opportunity to be included in prevention programming. Developing injury prevention programmes through an intersectionality lens is needed to better understand the factors that interact to influence an individual's risk for injury. There is a need to explore the unique experiences of youth at the intersection of various identity factors, including gender, race and ethnicity, and socioeconomic status. With this knowledge, prevention programmes can be more culturally responsive, gender transformative, inclusive, accessible and engaging for diverse groups of 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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".