Moving around the system: a way of working clinically using Bowen family systems theory
Bibliographic record
Abstract
Abstract Dr Murray Bowen, developer of Bowen family systems theory (BFST), had this to say to clients about working in family systems: if you get bogged down in one area, move into another (Bowen & Kerr, 1985). This statement, along with the knowledge of BFST, offered an inspiration for thinking about a method of therapy. This article highlights a method of working with an individual through a systemic lens. Two ideas are integral to this focus. One is that a client can move more easily into observing self within the system when they are not just observing self in one context or relationship but rather looking at how they function in different contexts or relationships. And second, by moving into different contexts of the system, the therapist is better able to manage the tendency towards symptom focus and stay centred on the work of differentiation. A therapist can truly have a stadium view of the system when speaking with the client about how they function in different areas of life. This method defined happens in three phases: in phase 1, the client observes themselves in their system in different contexts and looks for patterns in their functioning; in phase 2, the client takes this new self‐recognition and experiments with different ways of being in one context; and in phase 3, the insights gained from experiments in one context are applied in other contexts. Both client and clinician will benefit from less symptom focus and increased ability to observe patterns in relationships, both of which are core tenets of the work of differentiation in BFST.
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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 | 0.040 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".