Texting for Help: Processes and Impact of Text Counseling with Children and Youth with Suicide Ideation
Bibliographic record
Abstract
OBJECTIVE: To explore: (1) how children contacting a child helpline with suicide ideation differ from children discussing other topics, (2) whether text messaging effectively helps, and (3) which counselor behaviors are most effective. METHOD: Of 6,060 text sessions at the Danish national child helpline, 444 concerned suicidality, of which the 102 sessions that included self-rated, end Session ratings were selected for content analysis. RESULTS: Twenty-six percentage of suicidal children had severe suicidality. The suicide sample had significantly more girls, was older than the nonsuicide sample, and more often recontacted the helpline in the 2 weeks prior to follow-up. 35.9% of suicidal children felt better immediately and over half ended the session with a plan of action. At follow-up, 23.9% of suicidal children reported feeling better; however, 37.0% reported feeling worse. Talking about emotions, expressing empathy, and encouraging the child to talk to someone were associated with positive impacts. Setting boundaries was associated with negative impacts. CONCLUSIONS: Texting with suicidal children can be helpful, but should be considered a first step toward obtaining more sustainable help. Research is needed to determine how to better help children who felt worse or did not improve in the 2 weeks after contacting the helpline. Suggestions to further training of counselors 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".