Exploring crisis counsellor helping styles in online crisis counselling
Bibliographic record
Abstract
Crisis counselling via suicide prevention hotlines has demonstrated reduced distress and suicidality in individuals seeking support. Text-based online crisis counselling services (i.e., chats) are becoming an increasingly common way to support suicidal individuals in crisis. Existing research has not yet established a clear understanding of the crisis counselling process and how it relates to outcomes like reduced distress and suicidality. The purpose of this study was to build on current research by examining patterns of different crisis counsellor helping styles and exploring their associations with chatter outcomes, with the goal to develop a theoretical stage model of online crisis counselling. This study also explored whether crisis counsellor behaviours considered to be unhelpful were associated with a lack of chatter improvement. Past chat transcripts (N = 100) from a local crisis intervention centre were collected and coded for different crisis counsellor helping styles (i.e., active listening, collaborative problem-solving, and unhelpful) and chatter outcomes (i.e., affect, suicide risk, and suicide ideation). Analyses of variance were performed. Results indicated that active listening and collaborative problem-solving styles fluctuated over the course of chat, and some patterns of different crisis counsellor behaviours were associated with chatter outcome. Unhelpful crisis counsellor behaviours were associated with lack of chatter improvement. These findings contribute to the growing body of literature on online crisis counselling by generating a theoretical model of what the online crisis counselling process could look like, and how it may support suicidal individuals. Theoretical and practical implications are discussed.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".