Exploring the concurrent validity of a linguistically responsive pre-reading assessment in bilingual children: the Urdu Phonological Tele-Assessment (U-PASS) tool
Bibliographic record
Abstract
Introduction Childhood language and reading contribute to academic and socio-economic success. It is therefore important to identify school-aged children’s language and reading abilities as early as possible to provide additional support. Oral-language assessment tools are used by speech-language pathologists (SLPs) and educators for early identification of children at risk for reading difficulties. Particularly, phonological processing is an oral-language and pre-reading skill commonly used to examine reading skills at the early grade levels. However, there is a clear English-language assessment bias when it comes to these pre-reading assessment tools, which impacts clinical and research practices. Bilingual children are commonly misidentified with reading disorders–partially due to the lack of appropriate assessment tools. To address this bias, we developed and evaluated an age- and linguistically-appropriate Urdu Phonological Tele-Assessment (U-PASS) tool across three phases. The U-PASS consists of 10 phonological awareness, phonological memory, and rapid automatized naming subtests. Methods We tested 115 typically-developing Urdu-English simultaneous bilinguals in Grades 1–2 across Canada and Pakistan on the U-PASS, vocabulary and reading accuracy measures, and background questionnaires. Results Item-level analysis, involving point-biserial correlations, accuracy rates, and Cronbach’s alpha, and linear regression, involving subtest- and composite-level analysis, were used to collect evidence for U-PASS’s internal reliability and criterion-based concurrent validity. Discussion Our linear regression analyses indicated significant phonological processing-reading associations after accounting for background variables, thereby demonstrating U-PASS’s concurrent validity for assessing Urdu word/non-word reading. The open-access tool enables SLPs and educators to provide early pre-reading assessment and support for the successful development of oral-language and reading skills in bilingual children speaking Urdu—a common, yet under-investigated, language globally.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".