A Distress-Processing Model for Clients in Suicidal Crisis
Bibliographic record
Abstract
Abstract: Background: While crisis intervention frameworks have indicated the importance of clients in suicidal crisis better understanding their distress to decrease suicidality, it is unclear how clients in suicidal crisis process their distress. Aims: To develop (Study 1) and validate (Study 2) a sequential distress-processing model for clients in suicidal crisis. Methods: Applying task analysis, Study 1 consisted of three phases, which resulted in a theoretically and empirically informed model. In Study 2, we examined the distress-processing model’s validity using a longitudinal design. In both studies, data were online crisis chats with adults in suicidal crisis. Results: In Study 1, we developed a sequential five-stage distress-processing model: (Stage 1) unengaged with distress, (Stage 2) distress awareness, (Stage 3) distress clarity, (Stage 4) distress insight, and (Stage 5) applying distress insight. In Study 2, the model’s validity was supported via evidence that (H1) progression through the processing stages was sequential and (H2) clients with good outcomes had greater progression in their processing than clients with poor outcomes. Limitation: Clients who were suicidal but did not disclose their suicidality were not included. Conclusion: Our findings provide a framework for conceptualizing and operationalizing how clients move through suicidal crises, which can facilitate intervention and research developments.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".