Analysis of the child and adolescent needs and strengths assessment in a First Nation population
Bibliographic record
Abstract
First Nations youth are one of the fastest growing demographics in Canada, yet they are more likely to experience adverse health and life circumstances than non-Indigenous Canadians. Developing and implementing appropriate interventions for mental health is a priority area in decreasing this health gap, and requires the incorporation of First Nation models of mental wellness. Mental Wellness for First Nations? youth is tied to interpersonal and cultural factors such as relationships with caregivers and the greater community, caregiver and/or community access to necessary resources, and cultural identities. Examining these wider sociocultural factors, in combination with youth characteristics and strengths, provides a more comprehensive understanding of how to address mental health needs in First Nation communities. Working in collaboration with a First Nation based community health provider, the Child and Adolescents Needs and Strengths (CANS) assessment was analyzed for 178 First Nation children to identify specific mental health intervention needs and explore predictors of mental health needs. The CANS is a reliable measure that assesses youth mental health needs, caregiver needs, individual strengths, environmental strengths, as well as many other factors. The most commonly reported mental health intervention needs were seen for Anxiety, Mood, Emotional Control, and Adjustment to Trauma. Hierarchical regression identified referents? age, sex, Functioning, Individual Strengths, and Family/Caregiver Needs and Strengths domain scores as predictive of mental health intervention needs. Age and Functioning domain scores were robust individual predictors of mental health needs across most models, yet sex was not individually predictive in any model.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".