Indigenous Child Wellness:
Bibliographic record
Abstract
The measurement of wellness among Indigenous Peoples is crucial to understanding the needs of communities today and for generations to come. Here, we summarize the extant research on assessments relevant to measuring the wellness of Indigenous children in Canada through an examination of existing international best-practices. A thoughtful identification of wellness metrics aligned with Indigenous cultural contexts is important because in the past, wellness assessments that were not co-developed by Indigenous partners have perpetuated systemic harms. A scoping review of existing measures across Canada, the United States, Australia and New Zealand was completed consistent with the PRISMA guidelines across five databases. These guidelines provided guidance for the process of the review, as well as the structure for this paper. Search terms included "Indigenous" or "Aboriginal”, "wellness", “child-welfare”, "children”, “families" and “framework” or “measure”. In total 896 abstracts were screened. Of these, 88 articles were reviewed, 16 measures and four frameworks were identified as most relevant to our work. All efforts were led by Indigenous students in keeping with Traditional Ways of Being and Knowing as well as self-determination practices. Semi-structured interviews were also conducted with four Indigenous community members in order to advise the process of developing such a project and to gauge considerations on the appropriateness of assessing wellness in our communities. Results highlight a unique set of factors to consider from an Indigenous values perspective when assessing child wellness. The most salient of these include incorporating elements of self-determination in both measure development and usage. Themes of family, community, and wholism were also emphasized. While this exemplifies an emerging assessment base for measuring wellness, minimal work to date is directly designed to be relevant for Indigenous children or youth. Moving forward, we will seek to fill this gap by supporting the development of a wellness measure with potential to multi-contextual relevance to promote the adequate and equitable dispersion of supports and resources to families and communities.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".