Developing the ACEIG-scale: An adverse childhood experience scale for Inuit youth in Greenland
Bibliographic record
Abstract
BACKGROUND: Adverse Childhood Experiences (ACEs) have been identified as a major public health challenge in Greenland. No previous studies have created a multi-item ACE- scale among an Arctic Indigenous population. OBJECTIVE: To develop a multi-item ACE-scale among Inuit youth in Greenland (the ACEIG scale). METHODS: The ACEIG scale was created with data from the 'Wellbeing among Inuit youth in Greenland'-survey. Scale items were based on a recognised ACE-scale and further adapted to the context of Inuit youth in Greenland by adding items relevant for the population. The scale was validated through item response theory (IRT) and reliability was assessed by Cronbach's alpha. RESULTS: Four items relevant for Inuit youth in Greenland were added to the recognised ACE scale (bullying, death of parent, gambling problems in close family, and suicide in close relations). The scale was reduced by IRT, as three items (bullying, divorce of parents and parents passing away) exceeded the difficulty index threshold. The ACEIG scale therefore consists of 10 items: alcohol problems in close family, marijuana use in close family, domestic violence, being victim of physical violence, being victim of psychological violence, any type of sexual abuse, sexual abuse (intercourse), sexual abuse (more than once), suicide in close relations, and gambling problems in close family. Cronbach's alpha was 0.7. CONCLUSION: The ACEIG scale includes 10 items with acceptable reliability. The scale can inform future screening tools to identify vulnerable youth and target interventions. Future studies should investigate the association between the ACEIG scale and health outcomes.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".