Does establishing local treatment institutions lead to different populations seeking treatment among Greenlandic Inuit?
Bibliographic record
Abstract
Introduction: In 2016, a new addiction treatment service was established in Greenland to tackle the addiction problems with alcohol, cannabis and gambling among the population. The new service has established a treatment center in each of the five municipalities and works in partnership with a central private provider of treatment for those who reside in areas without a local treatment center.Methods: The national addiction database provided us with data from the Alcohol Use Disorder Identification Test, Alcohol Severity Index and questions on cannabis use and gambling behavior received at referral to, and at initiation of treatment. The data were analyzed for differences between the population in local or central treatment using SPSS version 25 (SPSS Inc., Chicago, IL).Results: Significant differences between the individuals in local and central treatment were revealed. Individuals in local treatment are more often women with minor children and a job, and their alcohol use is concentrated on weekends/holidays. Individuals in central treatment are more equal in both genders, few have minor children living at home, heavy drinking is more pronounced, and cannabis is used more frequently as well.Discussion: The findings support our expectations of local treatment being more attractive to individuals with obligations at home. The differences in the populations are worth considering when planning the treatment service, as the needs of the populations might differ. The findings are limited by many missing in the analyses, which we believe is caused by the establishing process of the new service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".