Systemic constellations applied in organisations: a systematic review
Bibliographic record
Abstract
Abstract This article—published in the Journal Gruppe. Interaktion. Organisation.— presents a systematic overview of the current empirical evidence of the effectiveness of the systemic constellation method when applied in organisations. Although the systemic constellation method is increasingly used for team coaching, organisational development and transformation processes, among others, scientific evidence on the effectiveness and quality of this method is still scarce. This may hamper the broader implementation of a potentially useful approach. Altogether, ten electronic databases were searched up to January, 2020. Multiple languages, qualitative and quantitative designs, and academic and grey literature were included. The search resulted in the identification of 79 potentially relevant publications, seven of which were prospective and 13 were retrospective effectiveness studies in terms of organisational outcomes. Only two of the seven prospective studies used a controlled design. This review concludes that the empirical evidence on the systemic organisational constellation method points toward a potentially effective intervention in the organisational context. However, it is too early to make firm conclusions as the number of studies was small and quality of the studies was low in general. The present systematic review summarises the literature on the systemic constellation method applied in organisations. It offers coaches and consultants insights into the method from a scientific perspective and describes potential mechanisms of action regarding the intervention. The results of the review provide a solid basis for future research and give directions for new studies to support quality improvement and help us better understand the factors influencing effectiveness.
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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 | 0.026 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".