The dynamics and processes of ‘ending’ in clinical supervision
Bibliographic record
Abstract
While there is a growing number of papers in academic and professional nursing journals that focus on clinical supervision, there remain unanswered questions and unresolved issues. One such issue where there is a distinct paucity of theoretical or empirical work is that of 'ending' within clinical supervision. Accordingly, this article examines key issues and dynamics of ending within clinical supervision. These key issues are summarized as: ending when the supervisor is reluctant to let go; parallel processes in ending in clinical supervision and ending in clinical relationships; the different ending dynamics of different approaches to clinical supervision; endings in group supervision; ending as a form of bereavement; healthy endings in clinical supervision; and endings as a opportunity for growth and celebration. The authors posit that an understanding of these processes can help facilitate a 'healthy' ending in clinical supervision. Furthermore, there may be particular merit in considering dynamics that, when present, create the best chance of all parties experiencing a health ending, namely: the ending is negotiated; the ending is gradual rather than sudden and all parties work towards the ending; the supervisee retains (wherever possible) a degree of control over the timing of the ending; and both supervisor and supervisee achieve a sense of closure.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".