Solution-focused therapy. Counseling model for busy family physicians.
Bibliographic record
Abstract
OBJECTIVE: To provide family doctors in busy office practices with a model for counseling compatible with patient-centred medicine, including the techniques, strategies, and questions necessary for implementation. QUALITY OF EVIDENCE: The MEDLINE database was searched from 1984 to 1999 using the terms psychotherapy in family practice, brief therapy in family practice, solution-focused therapy, and brief psychotherapy. A total of 170 relevant articles were identified; 75 abstracts were retrieved and a similar number of articles read. Additional resources included seminal books on solution-focused therapy (SFT), bibliographies of salient articles, participation in workshops on SFT, and observation of SFT counseling sessions taped by leaders in the field. MAIN MESSAGE: Solution-focused therapy's concentration on collaborative identification and amplification of patient strengths is the foundation upon which solutions to an array of problems are built. Solution-focused therapy offers simplicity, practicality, and relative ease of application. From the perspective of a new learner, MECSTAT provides a framework that facilitates development of skills. CONCLUSION: Solution-focused therapy recognizes that, even in the bleakest of circumstances, an emphasis on individual strength is empowering. In recognizing patients as experts in self-care, family physicians support and accentuate patient-driven change, and in so doing, are freed from the hopelessness and burnout that can accompany misplaced feelings of responsibility.
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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 | 0.004 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".