A mixed-method study of psychologists’ use of multicultural assessment.
Bibliographic record
Abstract
Despite practice guidelines and ethical standards that provide imperatives for clinicians to utilize multicultural assessment (MCA), little is known about how the average psychologist actually conducts MCA. The current mixed-method study was designed to investigate clinicians’ training and use of MCA practice strategies. Participants were 239 (107 male, 131 female, 1 other gender) licensed psychologists residing in the United States and Canada who were recruited from the American Psychological Association practice directory to complete an online survey. Quantitative items on the survey included questions about the number and utility of MCA-related graduate courses and supervision experiences, and strategies and frameworks used when conducting MCA. Open-ended questions provided expansion about factors that were helpful and not helpful in graduate training experiences. Findings suggested that only 75% of participants had taken a course that included MCA-related content, but almost all of those participants found the material they learned to be helpful. Graduate courses with MCA-related content were perceived as more helpful than graduate supervision, and the most helpful aspects of courses and supervision were related to increasing knowledge and awareness about MCA. Almost 40% of the sample reported using no theory or framework for conducting MCA, and participants differed in their use of MCA strategies. Findings are discussed in relation to the training and continuing education of clinicians and future directions for research.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".