The Relationship Between Client-Established Goals and Outcome in Counseling
Bibliographic record
Abstract
Use of goals as outcome measures has received some attention in the counseling literature, but little attention has been paid to the role of goal setting as a potential catalyst for change to enhance counseling outcome. Using a goal construct framework developed by Austin and Vancouver (1996), this research was a preliminary study to consider the relationship of client-established counseling goal content and dimensions (difficulty, specificity, and temporal range) to counseling outcome as measured by the Outcome Questionnaire (Lambert & Burlingame, 1996). Counseling goals of 51 participants (aged 18-25 years) were rated using the Ford and Nichols Taxonomy of Human Goals (Ford & Nichols, 1987; Ford, 1992); this research provides evidence of empirical validity for the use of this taxonomy to categorize counseling goals. A series of chi-square analyses, analyses of variance and linear regression analyses revealed relationships between goal specificity and counseling outcome. Client established counseling goal content was not related to counseling outcome. However, regardless of goal content, having specific goals was related to better counseling outcome, and if the content of the counseling goal had external consequences, it was critical to counseling outcome that the goal be set specifically. Counseling goals with internal consequences were more specific than those with external consequences, and in particular, affective goals were more specific than self-assertive social relationship goals. These findings are evidence that setting specific counseling goals can serve as a catalyst to increase the change occurring in counseling.
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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 | 0.019 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".