Language History Collection in Multilingual Clinical Practice: A Qualitative Analysis of Public-Sector Clinical Perspectives
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,059 | 0,079 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,012 | 0,017 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».