Bibliographic record
Abstract
Abstract One of the most promising areas of intervention for Solution-Focused Brief Therapy (SFBT) is with children, adolescents, and teachers in school settings. SFBT was applied in schools during the beginning of the 1990s and since that time the use of SFBT in schools has grown across disciplines with reports of SFBT interventions and programs implemented in schools in the United States, Canada, Europe, Australia, South Africa, and in the provinces of Mainland China and Taiwan. The brief and flexible nature of SFBT, and its applicability to work with diverse problems, make SFBT a practical intervention approach for social workers to use in schools. SFBT has been used in schools with student behavioral and emotional issues, academic problems, social skills, and dropout prevention. SFBT addresses the pressing needs of public school students that struggle with poverty, substance use, bullying, and teen pregnancy. It can be applied in group sessions, as well as individual ones, and in teacher consultations. There is also increasing empirical support that validates its use with students and teachers. SFBT has been applied to improve academic achievement, truancy, classroom disruptions, and substance use. The history and development of SFBT in schools, basic tenets of SFBT, the techniques that are used to help people change, and the current research are covered along with the implications for the practice of social work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".