Pandemic ripples:Scrutinizing Arctic communities’ perspectives on COVID-19 and mental health – A case against damage-culture
Bibliographic record
Abstract
It has been repeatedly pointed out that the long-term psychological consequences of the Covid-19 pandemic might still be underway, as the massive and complicated nexus of emotions and pain is only beginning to be understood. In the Arctic, the virus was not very widespread, and Greenland managed particularly well to control the pandemic and the imposed social isolation measures were limited. Yet, the numbers of suicide threats and cases of sexual and other forms of violence were reported to increase in Greenland, as well as across North American Arctic, particularly after the first wave of the pandemic. In this essay/blog, I explore emotional responses and psychological consequences of the pandemic for the Arctic communities. Further, by looking at assumptions about suicide, culture and cure, embedded in therapeutic and health discourses, I scrutinize the role of culture and context for mental health in Greenland. I also touch upon the danger of the ‘silent culture’ (in Danish, ‘tavseskultur’), ascribed to Inuit (and Sami) societies, and the possible side effects of the ‘culture of confession,’ which prevails in contemporary health care and media discourses, and which assumes that ‘talking’ is the only possible cure and hence, the only rational solution to the mental health problems in the Arctic. I question this universal idea, arguing that to change the current mental health challenges in the Arctic, we need to change the framework of understanding suffering experiences in relation to the conditions for people’s lives.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.044 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.012 |
| 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".