Establishing a Therapeutic Alliance With Youth Who Are Economically Disadvantaged: Misperceptions and Missed Opportunities
Bibliographic record
Abstract
Recognition of social class as a cultural construct is slowly emerging in the counselling literature. There are prominent social discourses that blame poor individuals for their disadvantage instead of considering the structural causes of poverty. School counsellors must avoid internalizing these beliefs and marginalizing students unintentionally. Social class is often invisible, so youth who are economically disadvantaged may go unnoticed and so may not receive the support they need to overcome systemic barriers and to experience success. The authors invite counsellors to examine and challenge their own biases and assumptions regarding individuals who live in poverty and to work within schools to provide culturally sensitive and socially just leadership. They propose that counsellors adopt the working-class values of openness and honest communication in order to facilitate the formation of therapeutic alliances with youth facing economic disadvantage. The authors highlight salient points from the current literature in order to raise class consciousness and propose advocacy at the micro, meso, and macro levels. They also invite readers to engage in critical reflection on their own beliefs and attitudes about youth who are economically disadvantaged as a foundation for their continued cultural competency development.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".