Peer Collaboration: A Model to Support Counsellor Self-Care
Bibliographic record
Abstract
In the context of a larger case study on how continuous learning in the workplace could be achieved through the implementation of peer collaboration, the process of how counsellors engaged in self-care within a large health care organization became clearer. This article isbased on data derived from a qualitative analysis of nine transcribed audiotaped meetings of the Counselling Trio, a group of three grief counsellors in a large urban health region. By describing the formation of the peer collaboration groups, the processes that led to the Counselling Trio’s successful collaboration, and the impact of the experience on the three participants, we attempt to illustrate how peer collaboration can be used as a forum for self-care among grief counsellors. We conclude with a critical reflection on the potentialof peer collaboration as a vehicle for organizational support of counsellor self-care.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".