Indicators of Social and Emotional Well-being among Indigenous Children in Australia, Canada, New Zealand, and the United States: Protocol for a Scoping Review
Bibliographic record
Abstract
Objective: The objective of this scoping review is to identify, describe, and consolidate indicators that have been developed to evaluate the social and emotional well-being (SEWB) of Indigenous children in Australia, Canada, New Zealand, and the United States. This review will also explore the application of identified indicators to early childhood (children aged 0-6) as well as the engagement of Indigenous peoples in the process of developing identified indicators. Introduction: Indigenous children experience health inequalities that are shaped by the historical and enduring experiences of colonisation. SEWB is a construct that considers the impacts of colonialism and other structural determinants on Indigenous children’s health, as well as sources of strength, resilience, and healing to overcome health inequalities. There is growing consensus that Indigenous communities must meaningfully engage in the creation of culturally responsive indicators to measure Indigenous children’s health and well-being. Eligibility criteria: Using the Population-Concept-Context (PCC) framework, this review will focus on indicators of SEWB (Concept) among Indigenous children (Population) in Australia, Canada, New Zealand, and the United States (Context). Methods: This proposed scoping review will be conducted in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews. This scoping review will consider peer-reviewed publications and grey literature sources that meet the eligibility criteria. Evidence sources published in the English language between 2004 and 2021 will be included. Results will be presented in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews (PRISMA-ScR) checklist and using tables, figures, and narrative text.
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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 | 0.118 | 0.107 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".