Well-being among indigenous children and youth in the Arctic – with a focus on Sami and Greenland Inuit
Bibliographic record
Abstract
Introduction: Children and youth in the Arctic regions of the Nordic countries represent Indigenous Peoples and are in some aspects a vulnerable group. The aim of this study was to scope the literature to identify knowledge gaps, action taken and needed, as well as directions for future research and interventions in regards to indicators for the wellbeing of indigenous children and youth in the Arctic region of the Nordic countries and Greenland. Methods: Literature (both scientific and grey) on the well-being among children and youth in the Arctic region was reviewed. The search was limited to the timeframe 2009-2017. A search syntax inspired by Augustsson and Hagquist was applied in the databases PubMed/MEDLINE and PsycINFO. Results: From 247 scientific articles focusing on well-being of children and youth, 27 were found relevant to the Arctic context. Additionally, 31 documents and 46 homepages categorized as grey, non-peer-reviewed literature were reviewed of which 28 sources were selected. Discussion: The findings of the scoping review indicate that the focus on indigenous children’s and youth’s well-being in the Nordic countries is limited. This study reveals how the efforts for promoting children’s and youth’s well-being and mental health in the Arctic need to be developed in close collaboration with the local population in the Arctic region, children as well as adults. The evidence for actions and initiatives with a focus on involving cultural identity, skills in the nature and local connection are increasing in the north American literature, but hardly existing in literature on Sápmi and Greenland.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".