A Descriptive Qualitative Approach to Understanding Psychologists’ Process Assessing English Language Learners
Bibliographic record
Abstract
Psychological assessment is a cornerstone of evidence-based practice, offering critical insights that can foster growth, enhance self-esteem, and identify areas for effective intervention. Despite the importance of assessment to psychological practice, assessment has historically received less research attention than intervention practices, leaving significant gaps in understanding and optimizing this process, particularly in the context of diverse populations. This issue is particularly pressing in Canada, where an increasingly multicultural and multilingual population presents unique challenges for psychologists conducting culturally responsive and evidence-based assessments. Language, a key aspect of diversity, plays a pivotal role in the assessment process, influencing rapport-building, test administration, diagnostic accuracy, and intervention planning. However, psychologists often lack adequate training and understanding to navigate the complexities of language diversity effectively, resulting in potential disparities in service quality for linguistically diverse populations, including English language learners (ELLs). This study investigated how psychologists conduct assessments with ELLs, focusing on their training experiences, strategies to navigate linguistic complexities and assessment practices. Using a descriptive qualitative approach, Canadian psychologists (N = 4) described their experiences through in-depth interviews. The participants also submitted clinical process notes based on an assessment case with an ELL client and completed a demographic questionnaire to provide additional context about their training and assessment practices. Findings highlight significant gaps in psychologists' preparedness to conduct psychological assessments with ELLs, including limited exposure to second-language acquisition knowledge and training regarding assessments with linguistically diverse populations. The participants described the need to adapt assessment methods when working with ELL clients and ensuring they are engaging in culturally responsive practices to make informed clinical decisions and recommendations for interventions tailored to their client's needs. The findings underscore the role of clinical reasoning and decision-making in integrating diverse data sources and addressing biases in standardized tests. Recommendations for training and practice emphasize the importance of enhanced graduate training, professional development opportunities, and evidence-based guidelines for assessing ELLs, ultimately improving psychologists' ability to deliver more inclusive and practical assessments. This work contributes to the field by offering insights that can bridge the training-to-competency gap and promote culturally responsive psychological assessment in Canada's increasingly diverse society.
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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 | 0.032 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".