Evaluating the effects of cultural immersion on counselor trainees' multicultural development and intercultural competence: A metasynthesis of qualitative evidence
Bibliographic record
Abstract
This metasynthesis critically surveyed and evaluated the learning impacts on counselor and psychology trainees’ multicultural development and intercultural competence through participating in cultural immersion (CI), based on published qualitative research evidence. Accordingly, this metasynthesis identified and assessed the characteristics, the methodological strengths and qualities, and the thematic findings of 33 qualitative and mixed-methods CI studies resulting from exhaustive database searches. Using a directed content analysis technique, a six-domain analytical framework was applied to code and analyze the themes reported in these studies. The results point to CI intervention as a multifaceted and versatile instructional apparatus that impacted and contributed to trainees’ multicultural development and intercultural learning multidimensionally, across cognitive, perceptual-attitudinal, affective, and skills-behavioral domains. These learning outcomes include trainees’ increased cultural awareness and knowledge (cognitive), enhanced reflexivity on their worldview, positionality, and attitude (perceptual-attitudinal), heightened emotion and growth in cultural empathy (affective), adaptation and display of new behaviors and relational skills and increased multicultural competence (skills-behavioral). Therefore, CI embodies many favorable characteristics of experientially-based learning as stipulated in the existing multicultural counseling and intercultural training literature. These findings lend nuanced empirical support for the application of CI to facilitate counselor trainees’ multicultural orientation, development, and skills, and offer insights into structural facilitators for enhancing immersion training. However, a lack of structural and methodological consistency and theoretical depth among the existing CI studies were observed as major limitations. Implications and recommendations for advancing future CI and multicultural training practice and research are discussed.
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 | 0.073 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".