What twitter can tell us about user experiences of crisis text lines: A qualitative study
Bibliographic record
Abstract
Mental health problems are the leading cause of disability worldwide. Despite the prevalence and cost of mental illness, there are insufficient health services to meet this demand. Crisis hotlines have a number of advantages for addressing mental health challenges and reducing barriers to support. Mental health crisis services have recently expanded beyond telephone hotlines to include other communication modalities such as chat and text messaging services, largely in response to the increased use of mobile phones and text messaging for social communication. Despite the high uptake of crisis text line services (CTLs) and rising mental health problems worldwide, CTLs remain understudied. The current study aimed to address an urgent need to evaluate user experiences with text-based crisis services. This study explored user experiences of CTLs by accessing users' publicly available Twitter posts that describe personal use and experience with CTLs. Data were qualitatively analyzed using thematic analysis. Six main themes were identified from 776 tweets: (1) approval of CTLs, (2) helpful counselling, (3) invalidating or unhelpful counselling, (4) problems with how the service is delivered, (5) features of the service that facilitate accessibility, and (6) indication that the service suits multiple needs. Overall, results provide evidence for the value of text-based crisis support, as many users reported positive experiences of effective counselling that provided helpful coping skills, de-escalation, and reduction of harm. Results also identified areas for improvement, particularly ensuring more timely service delivery and effective communication of empathy. Text-based services may require targeted training to apply methods that effectively convey empathy in this medium. Moving forward, CTL services will require systematic attention in the clinical research literature to ensure their continued success and popularity among users.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".