A comparison of two systemic family therapy reflecting team interventions
Bibliographic record
Abstract
This research focused on exploring the experience of fifteen families who attended a first session of systemic family therapy, with reflecting team feedback being delivered in two different formats. In the first the interviewer consulted with the reflecting team alone after the therapy session while the family took a break and then provided feedback directly to the family. In the second the interviewer and family exchanged rooms straight after the session and the reflecting team provided feedback in conversation with each other. Families in both conditions of systemic family therapy described how the presence of a team led to a heightening of their emotion in session, a factor that served to effect a change in family interaction. For families in the first condition the consultation break reduced these intense emotions and provided a unique opportunity to continue independent discussions outside the therapy room. In contrast, those in the second condition reported that their experience was more exciting but it made information retention difficult. For this reason, the use of a therapeutic letter with this group was a crucial aspect of follow‐up intervention. Practitioner points Families see systemic family therapy reflecting teams as helpful, providing an opportunity for new conversations, perspectives and behaviour to emerge. These benefits can be maximized by providing a detailed descriptions of the process beforehand, minimizing phone calls from the reflecting team to the interviewer and providing a break between interview and feedback. When using Andersen's (1991) model, following‐up sessions with a therapeutic letter can enhance the retention of information.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".