Assessing Attachment Classification Difference Between Indigenous and Non-Indigenous Children: A Systematic Review
Bibliographic record
Abstract
This systematic review sought to investigate differences in attachment classification between Indigenous and non-Indigenous children, measures used to determine Indigenous attachment classification, and adjustments made to measurements to ensure relevance to Indigenous participants. Owing to diversity in cultural context and caregiving arrangements between Indigenous and non-Indigenous populations, classification differences were expected between cohorts. A systematic search of 5,980 studies was undertaken to investigate attachment classification differences between Indigenous and non-Indigenous infants and children, age 0 to 18 years, in Australia, New Zealand, Canada, and America, in which 15 eligible quantitative and qualitative studies were identified and synthesized ( N = 3,452). There were no suitable Australian, New Zealand, or Canadian studies utilizing Indigenous samples. The review relied on Native American infant and child samples and found a lack of culturally specific attachment classifications, as studies did not distinguish between Indigenous and non-Indigenous participant classifications. Furthermore, studies did not validate adjustments made to attachment measures or designs to ensure contextual relevance and applicability to Indigenous participants. The review brings to attention the lack of culturally specific attachment measures for Indigenous infants and children. Further research is needed to establish a reliable attachment classification system for use with Indigenous infants and children to ensure a comprehensive and informed understanding of Indigenous caregiving systems and assessment and influence key decisions that impact the wellbeing of Indigenous people.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".