Make every session count for clients! Rethinking clinical social work practice from Single Session Therapy (SST): A case illustration of Emotion-Focused Therapy (EFT)
Bibliographic record
Abstract
Re-thinking a service delivery paradigm within a single session therapy (SST) framework inevitably changes the ways we consider the therapeutic process – making each session count! In clinical practice, this means (1) the rapid development of therapeutic alliance and (2) the life of the therapy process, from assessment to intervention to evaluation condensed into a single session. To illustrate, this article analyzes the fully transcribed one of the master tapes in Emotion-Focused Therapy where chair work is demonstrated in a single session, and finds six processes in SST: (1) history taking; (2) formulation; (3) contracting; (4) working-through change; (5) evaluation; and (6) preparing for exit. A micro-analysis of the single session details how the therapist manages multiple tasks in SST. This analysis helps social workers to re-think service delivery and attrition rates in community mental health settings to enhance health equity and the provision of socially just mental health services.
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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.035 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".