Latent Class Analysis of upbringing and mental health status among youth and young adults in Greenland
Bibliographic record
Abstract
Background\nProfound socio-cultural changes in Grenland during the last 70 years have resulted in radical\nchanges in the health and well-being of the population. Suicide rates and mental health problems\nhave been rising, particularly among the young part of the Greenland Inuit. Previous research\nhas found that protective factors for mental health in the Arctic often are linked to traditional\nactivities and Inuit culture. Yet, most previous research has been assessing the youth in general,\nand more knowledge is needed, on how the youth and young adults in Greenland differ.\n\nDesign and Methods\nThis thesis was made as a cross-sectional study with data from the Greenland Health Survey\n2018 and included 658 respondents between 15-34 years. The thesis sought to investigate\nhow conditions in upbringing characterise Greenlandic youth and young adults with different\nnegative and positive mental health outcomes. This was done by latent class analysis with distal\nvariables.\n\nResults\nFour subgroups of youth and young adults were identified based on conditions in upbringing.\nClass 1 (n=178, 27%) and Class 2 (n=164, 25%) were both characterised by a relatively low\nprobability of adverse childhood experiences (ACEs) during upbringing and a high probability\nof having grown up with strong ties to Inuit culture. These classes had the lowest probability of\nnegative mental health outcomes. Class 3 (n= 224, 34 %) grew up with the highest probability\nof having experienced ACEs during upbringing and the highest probability of negative mental\nhealth. Class 4 (n= 92, 14 %) had the lowest probability of having grown up with strong ties to\nInuit culture and the second-highest probability of an upbringing with ACEs.\n\nConclusion\nIndividuals growing up with the combination of an absence of ACEs and with strong ties to\nInuit culture have the best mental health outcomes. This combination fosters good mental health\nindependent of whether the individuals grew up in a settlement or town and independent of\nDanish language proficiency. Individuals growing up with ACEs have the poorest mental health\noutcomes. A significant proportion of the youth and young adults in Greenland have ACEs, and\nthe ACEs investigated often co-occur.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".