Clinical Supervision, Workplace Culture, and Therapeutic Engagements with Youth at Risk for Suicide
Bibliographic record
Abstract
Clinical supervision practices and workplace cultures are highly influential in shaping the quality of care that clients at risk for suicide receive. The purpose of this qualitative study was to document counselors’ and supervisors’ views regarding the supervisory practices and conditions that enable useful and life-enhancing practices when working with youth who struggle with living. We conducted two, 1.5-hour focus groups with counselors who work with youth at risk for suicide in two separate clinical sites in British Columbia, representing the views of seven counselors. Two clinical supervisors who each supervise a small team of counselors were interviewed individually. Reflexive thematic analysis, informed by a constructionist lens was used to organize and interpret the qualitative data, with a strong emphasis on researcher subjectivity, reflexivity, and the contextual nature of meaning-making. Four themes were generated: being guided by a flexible framework; addressing anxiety in a systemic way; resourcing self and others; and expanding possibilities for engaging with complexity. We argue in favor of flexible, relational, and reflexive approaches to supervision, where the development of useful responses to suicidal behavior can best be understood as a shared, system-wide responsibility, which does not place the onus for “saving lives” onto the shoulders of individual counselors.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".