Integration of the learning process and the group development process in group supervision
Bibliographic record
Abstract
This article focuses on group supervision, inviting readers to take an interest in two ongoing processes: the group process, i.e., the stages of group development, and the integration of the learning process. These processes occur as the group is reflecting on issues brought to it. Supervisees do not realize that they encounter, and are even often affected by, the intertwining of these two processes; however, supervisors should be aware of this in their desire to guide their supervisees skilfully.In addition to presenting an explanation of these processes, this article highlights conditions of success for group supervision: clarification of the institution’s mandate and the status of the supervisor, elements to be considered when setting up group supervision, the importance of negotiating the contract, and the characteristics of the group at work. Although no existing theory integrates the entirety of the practice of group supervision, we have attempted to demonstrate links between these two processes by drawing on both our own experience and a conceptual framework. We believe that our conception of group supervision is worth validating through future research.SommaireLe présent article illustre comment les processus d’intégration des apprentissages et les phases d’un développement d’un groupe s’entrecroisent et s’inter-influencent dans un groupe de supervision. Après avoir traité desdits processus, les auteurs décrivent les principaux phénomènes issus de cet amalgame. L’article se termine en décrivant les conditions de réussite d’une supervision de groupe.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".