Part of the problem or part of the solution? Communication assessments of Aboriginal children residing in remote communities using videoconferencing
Bibliographic record
Abstract
The current article describes the results, inter-scorer reliability, and potential sources of bias in conducting speech-language assessments with Aboriginal children in remote Ontario communities using videoconferencing. A main focus of this pilot study was to examine scoring bias, an issue that might arise with videoconferencing for any population but that could potentially interact with test and cultural bias to negatively affect the diagnosis of Aboriginal children. Assessments were administered by a remote-site speech-language pathologist (SLP), while an on-site SLP served as an assistant. Responses were scored simultaneously by both SLPs and the results and their degree of correspondence were compared. Percentage agreement ranged from 96-100% for language tests and from 66-100% for the articulation measure. Results suggest that videoconferencing can be an effective complement to service provision when procedures are organized so as to minimize bias in test administration and in the interpretation of test performance.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".