Exploring alliance–outcome associations in a combined mental health and academic counselling setting for at‐risk students: The differential role of alliance components, personality, gender and pre‐treatment severity
Bibliographic record
Abstract
Abstract This study investigated the differential role of therapeutic alliance components in predicting mental health and academic performance outcomes of a combined academic and personal counselling initiative. The study also explored the role of pre‐treatment severity and gender in alliance formation, as well as the moderating role of personality on alliance‐outcome associations in at‐risk students at a Canadian University. Pre‐ and post‐measures of mental health (SF‐36 Mental Health Composite Summary) and academic performance (GPA) were obtained for 78 at‐risk students enrolled in the counselling initiative. Criteria for risk included academic struggles, struggles with mental health, or both. Personality and demographic variables were obtained prior to the start of counselling and strength of therapeutic alliance was measured between sessions 3 and 6. Results showed that while all of the alliance components significantly predicted increases in student GPA, the task subscale predicted increases in mental health. Personality type preference was not found to moderate these associations. Furthermore, gender and pre‐symptom severity were not related to early alliance formation. The findings support the importance of the therapeutic alliance across counselling contexts and suggest that relationship oriented counselling initiatives may support both academic performance as well as psychological well‐being in at‐risk students. Implications are discussed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".