Effectiveness of Solution Focused Therapy for Affective and Relationship Problems in a Private Practice Context
Bibliographic record
Abstract
The data from 277 cases, 140 males and 137 females, from the case load of a clinical psychologist, with over ten years experience who used future oriented solution focused therapy was analyzed with re-gard to clinical variables that are related to the number of sessions attended and the degree of problem resolution. The number of presenting problems, number of stressors, and taking medications were found to be predictive of attending more sessions. Poorer functioning at intake and greater number of late cancels were predictive of poorer problem resolution, accounting for 18.7% of the variance. Those persons presenting with affective disorders attended an average of 4.14 sessions, with 60.9% partially or mostly resolving their presenting problem. Those who presented with non-affective/relationship problems attended an average of 2.34 sessions, with 76% partially or mostly resolving their presenting problem. This study, using a substantially greater data base than Lambert, Okiishi, Finch, & Johnson (1998), found similar percentages of symptom reduction with a relatively low average number of sessions that would fit within most managed health care session allotments and within many employee assistance program benefit caps.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".