Exploring developmental screening practices with Indigenous early intervention programs in British Columbia: An exploratory, qualitative study
Bibliographic record
Abstract
The purpose of this exploratory qualitative research project was to explore developmental screening with professionals in two Indigenous early intervention programs in British Columbia (BC), the Aboriginal Infant Development Program (AIDP) and Aboriginal Supported Child Development Program (ASCD). The research was developed in collaboration with the Provincial Advisors of AIDP and ASCD and supported by their knowledge and experience. Focus groups and interviews undertaken in 2021, gathered the perspectives and experiences of AIDP and ASCD professionals (n=8) on developmental screening and how screening tools are used with Indigenous children and families. Analysis of the findings identified the following main themes: a) professionals reflecting on ‘how effective is using a screening tool’ without a relationship; b) respecting that the family steers the way; c) the importance of adapting how the screening tool is used, and d) managing the pressure of professionals moving forward. Professionals focused on the relationships built with families and the process of how a screening tool is used, rather than the tool itself. The themes apply to a broad range of early childhood programs, serving Indigenous and non-Indigenous families. Future considerations for practices are provided for individuals, organizations, and further research.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".