Thriving Beyond Adversity: A Prospective Longitudinal Cohort Study Using a Strength-Based Approach Depicts Indigenous Adolescents with Less Adverse Childhood Experiences (ACEs) Had Fewer Neurodevelopmental Disorders (NDDs)
Bibliographic record
Abstract
Improving social and emotional well-being (SEWB) among Indigenous adolescents is crucial. Since neurodevelopmental disorders (NDDs) are common in Indigenous people and adverse childhood experiences (ACEs) are important contributors to negative health outcomes throughout the lifespan, we investigated whether limited ACE exposure is associated with reduced risk of NDDs in Australian Indigenous teens using the data from multiple waves (Wave 1 to Wave 9, and Wave 11) of the Longitudinal Study of Indigenous Children (LSIC). We also examined the role of other protective factors, such as Indigenous cultural identity and school connectedness, against NDDs. A strengths-based approach using mixed-effects logistic regression models examined the protective effect of limited ACE exposure (from LSIC waves 1–9) on NDDs (outcome from LSIC wave 11), adjusting for sociodemographic factors. The NDDs included autism, ADHD, intellectual, neurological, and specific learning disabilities. Of the 370 individuals analysed, 73.2% valued Indigenous cultural identity, and 70.5% were strongly connected at school. More than one-fourth (27.8%) reported limited ACE exposure, while the majority was not diagnosed with NDDs (93%). Longitudinal analysis revealed limited ACE exposure was 6.01 times (95% CI: 1.26–28.61; p = 0.024) more likely to be protective against NDDs compared to those exposed to multiple ACEs. Moreover, valuing cultural identity (aOR = 2.81; 95% CI: 1.06–7.39; p = 0.038) and girls (aOR = 13.88; 95% CI: 3.06–62.84; p = 0.001) were protective against NDDs compared to their respective counterparts. Our findings highlight the need to prevent ACE exposure and promote Indigenous cultural identity in preventing negative health outcomes and the exacerbation of health inequities to strengthen the SEWB of Indigenous communities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".