Critical Incidents in Graduate Student Development of Multicultural and Social Justice Competency
Bibliographic record
Abstract
In many countries, counselling psychology has increasingly attended to the diverse needs of clients within multicultural societies. In North America, emphasis has been placed on nondominant populations, whose experiences of discrimination, cultural oppression, and social, economic, and political marginalization, have a significant impact on psychosocial wellbeing. Counsellors are expected to engage in social justice action, with or on behalf of clients, to effect change in organizations, communities, or broader social systems. The purpose of this study was to examine how well counsellor education programs in Canada are preparing counsellors for both multicultural counselling and social justice. Most research has focused on curriculum content; less attention has been paid to how that content is taught and the efficacy of those learning processes in facilitating competency. The critical incident technique was used to solicit examples of effective and less effective learning processes from graduate students. The qualitative data was analyzed to isolate, cluster, and relate emergent concepts; a critical lens also highlighted missing constructs. The most common learning contexts were the multicultural counselling course and the applied practicum. Four themes emerged that reflect current research: fostering of self-awareness, application of theory to practice, engagement of affect through experiential exercises, and exposure to diversity. Infusion of multicultural and social justice competency throughout curricula is also widely advocated, but was reflected in only four percent of the critical incidents. Engagement in social justice roles and activities was also missing. Enhancements to both curriculum content and process are required to support the social justice agenda. DOI: 10.5901/ajis.2013.v2n9p105
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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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".