A Multi-Dimensional View on Mental Distress of Alaskan Adults
Bibliographic record
Abstract
Mental health impacts every facet of day-to-day life, and therefore it is important to determine what factors influence mental wellbeing in order to best target those areas to improve individuals’ mood and health. This paper analyzes how mental distress rates in Alaskan counties are affected by environmental and societal issues such as latitude, insufficient sleep, access to healthy food, presence of severe housing issues and rates of physical inactivity. By specifically studying mental distress in Alaskan residents, the extremes of day length and population size bring a new dimension to previous research done on mental distress. The variables were derived from County Health Ranking’s 2020 data for Alaskan counties. The rates of frequent mental distress between Alaska (12.01%) and Florida counties (13.64%) in 2020 were significantly different (t = -3.671, p < .001) indicating latitude is related to mental distress. A Pearson correlation (r = .641, p > .001) indicates that as the percentage of those with insufficient sleep increase, the percentage of those with frequent mental distress also increases. Percent of population with severe housing problems (B = .118, t = 5.465, p < .001) and percent with limited access to healthy food (B = .052, t = 2.350, p = .027) significantly contributed to the best fitting step wise linear regression model (F2,26 = 51.957, p < .001) model to account for the variance in the percent of frequent mental distress in Alaska counties in 2020, encompassing 80.0% of the variance in the rate of frequent mental distress.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".