How the helping process unfolds for clients in suicidal crises: Linking helping‐style trajectories with outcomes in online crisis chats
Bibliographic record
Abstract
OBJECTIVES: Crisis counselors' active listening and collaborative problem-solving helping styles have been associated with outcomes for clients in suicidal crises. These associations have been based on static conceptualizations of helping (i.e., helping style for the entire session). Our aim was to further understand how the crisis counseling helping process unfolds (i.e., helping trajectory) and helping trajectories' association with clients' outcomes. METHODS: Online crisis chats (N = 269) with suicidal adults were coded for crisis counselors' helping styles (i.e., active listening and collaborative problem-solving) and clients' outcomes (i.e., resolved or unresolved). Each talk-turn was coded for helping style, which were used to examine helping-style trajectories. RESULTS: Growth-curve models indicated that helping styles varied over the course of chats and that helping trajectories were different for resolved and unresolved chats. In resolved chats, helping styles moved from primarily active listening to primarily problem-solving-with a deceleration in the middle of chats. In unresolved chats, helping initially moved from primarily active listening to primarily problem-solving, but this trajectory decelerated in the middle of chats and then turned back toward primarily active listening. CONCLUSION: Our findings demonstrate that how the helping process unfolds is related to clients' outcomes. Implications for practice and research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".