Language History Collection in Multilingual Clinical Practice: A Qualitative Analysis of Public-Sector Clinical Perspectives
Bibliographic record
Abstract
Abstract Background Clinicians increasingly work with multilingual paediatric clients across healthcare and community settings. Collecting detailed language background is a crucial first step in planning effective assessment and intervention. Yet, little is known about how this process unfolds in everyday public-sector clinical practice. To improve service quality, equity, and effectiveness for multilingual children, this study investigates how clinicians gather, interpret, and use language history information: as well, it examines the institutional and professional barriers and facilitators that shape this aspect of clinical practice. Methods A qualitative study was conducted using semi-structured interviews with 21 clinicians working in public-sector and community-based settings across Canada. Data was analysed using framework analysis, guided by the Theoretical Domains Framework. Results Clinicians universally recognized the value of language history and routinely embedded it within the broader case history. However, variability emerged in what information was gathered, how it was elicited, and how it was used. Practices were shaped by clinician experience, institutional processes, documentation systems, and availability of training and tools. Many relied on flexible, conversational strategies over research-developed tools, often constructing their own frameworks in response to contextual demands. This adaptability reflected the development of adaptive expertise but also risked inconsistencies in data quality, especially in the absence of formal guidance, structured tools, or interpreter support. Conclusion Language history collection is a complex, multidimensional task influenced by clinician initiative and systemic constraints. Strengthening practice will require hybrid tools that balance structure with flexibility, clearer protocols across disciplines, and institutional investments in interpreter services, training, and culturally informed workflows. WHAT THIS PAPER ADDS Section 1: What is already known on this subject Collecting detailed language history is essential when assessing multilingual children, as it serves as a foundational step in guiding service delivery. Existing research-developed questionnaires (e.g., LEAP-Q, ALDeQ) provide structured ways of gathering this information, but evidence about how clinicians actually collect and use language history in everyday practice is limited. Section 2: What this paper adds to existing knowledge This study provides qualitative evidence on how public-sector clinicians in Canada gather, interpret, and apply language history information with multilingual children. It shows that while language history is universally valued, practices vary widely depending on clinician experience, institutional systems, and resource availability. Findings highlight both adaptive strategies and systemic gaps, pointing to the need for hybrid approaches that combine structure with flexibility. Section 3: What are the potential or actual clinical implications of this work? This study showed that while clinicians’ flexible, conversational strategies promote rapport and cultural responsiveness, they may also create variability and leave gaps in completeness and reliability. Clinicians can integrate hybrid approaches that combine structure with adaptability to support gathering more consistent and clinically useful information. At the system level, standardized documentation protocols, access to trained interpreters, and interprofessional training are critical supports necessary for embedding consistent, high-quality language history collection across services.
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 | 0.059 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".