Learning to be healthy: An Aboriginal youth perspective
Bibliographic record
Abstract
H ealthy isn't something that you are; healthy is some- thing that you learn to be.Aboriginal youth face higher risks of negative health factors than any other ethnic group in Canada (1,2).The one thing that separates the Aboriginal youth population and their health statistics from other groups in Canada is the fact that most, if not all, of the health risks can be directly or indirectly attributed to recent actions in history.My sister Karen is 18 years old and a grade 12 student.I asked her to share her perspective and some of her experiences with me.As a young person, she sees her friends unwinding in unhealthy ways, such as smoking, using alcohol and doing drugs, and there are constant negative comments and attitudes coming from teachers and other students.She feels the mental health of other Aboriginal students is not doing all that well either.The sad part is that these behaviours and their negative effects are accepted as the norm by both those who participate in the behaviours and those who observe them (ie, other students, teachers and others).Only a few who are working hard to change these behaviours truly understand the underlying causes.Those are one person's observations.There are also the statistics.Aboriginal youth are the fastest growing demographic in Canada (1).Social workers remove Aboriginal children from their homes at a faster rate than non-Aboriginal children (2).Aboriginal children and youth face higher rates of suicide, hepatitis C, HIV/AIDS, cancer, improper tobacco and alcohol use, obesity, accidental injury and depression (1).Not only are these issues affecting Aboriginal children on the surface, there is also the fact that in First Nations (both on and off reserve), Métis and Inuit communities, Aboriginal youth receive less funding and less programming and have access to fewer resources than the average young person in Canada (2).Healthy isn't just something you are, it is something you learn to be, and it is really hard to be healthy when there is no way to learn to be so.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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".