Karl Tomm’s Collaborative Approaches to Counselling
Bibliographic record
Abstract
Karl Tomm, a Canadian psychiatrist and family counsellor, has been at the forefront of developments in collaborative practice with clients for over 25 years. We situate Dr. Tomm’s ideas in relation to counselling, noting some of his important contributions to conceptualizing counsellors ’ engagement with clients. We will also depict Tomm’s innova-tions as dialogic, suggesting that he looks upon his interactions with clients as co-creating accomplishments in the back and forth of therapeutic interaction. We highlight detailed discursive analyses of his actual interviews with clients, illustrating some conversational practices that further collaborative practice. The article concludes by summarizing Tomm’s ideas and conversational practices as they relate to fostering collaborative dialogue with clients. résumé Karl Tomm, un psychiatre et un thérapeute familial Canadien, a été au centre des dévelop-pements en pratiques collaboratives avec clients pour plus de 25 ans. Nous présentons les idées significatives en counselling de Dr. Tomm pour conceptualiser et faciliter le rapport
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".