Why did you use that test? Exploring speech-language pathologists clinical decision-making in bilingual language and literacy assessment
Bibliographic record
Abstract
Abstract Purpose Our overarching goal is to advance our understanding of clinical decision-making processes in bilingual language and literacy assessment. When evaluating bilingual children, speech-language pathologists (SLPs) use static norm-referenced assessments (SAs) developed for English monolinguals more frequently than less biased dynamic assessments (DAs). To date, no research has considered why SLPs use SAs over DAs or examined SLPs’ conceptualization of validity beyond knowledge of psychometrics. In this study we explore factors that affect SLPs’ choice and use of assessments and how clinicians conceptualize and employ validity through the lens of modern validity frameworks. Method Canadian SLPs (n=21) participated in semi-structured interviews, using a guide informed by the Theoretical Domains frameworks and Kane’s Validity framework. Reflexive thematic analysis was used to generate themes. Results Clinicians rarely report using “dynamic assessment” but did “assess dynamically” by incorporating teaching in testing. When assessing oral language, SLPs acknowledged that using SAs with bilinguals may be inappropriate, but that they continue to do primarily because scores from these measures are necessary for diagnosis and accessing services. To contend with this friction between clinical beliefs and workplace requirements, most SLPs report caveats alongside SA scores SAs to contextualize findings. Though individual clinical knowledge of psychometrics and validity in assessment varies, systemic issues play a key role in perpetuating current assessment practices with bilinguals. Finally, bilingual literacy assessment practices differ. Clinicians use a wider variety of assessments and rely less on scores to achieve desired outcomes for students. Conclusion Clinical decision-making in bilingual language and literacy assessment is influenced by both individual and contextual factors. Accordingly, efforts to shift practice patterns cannot solely focus on individual clinical knowledge but must also examine and address these systemic issues.
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 | 0.041 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".