Use of suicide prevention helpline services by first-time, frequent, and daily callers: A national cohort study
Bibliographic record
Abstract
OBJECTIVE: The objective was to examine response rates, types of callers and their probability of being answered, prevalence of at-risk callers, and to calculate national call rates. METHODS: Data on all calls to the Danish, national telephone helpline for suicide prevention during July 2019 to December 2022 were analysed. A measure of unique calls was developed to account for repeat calls not being answered. We examined the probability of calls being answered by caller types using logistic regression and calculated national call rates for individuals aged ⩾15 years. RESULTS: Overall, 526,533 calls were made by 31,317 individuals, and 131,621 unique calls were identified, of which 48.9% were answered. First-time callers (95.1%) accounted for 5.7% of calls. We found that 0.1% of callers accounted for 61.8% of all calls. This group of daily callers (>1000 calls each year) consisted of 8-12 unique callers and was more likely to be answered (odds ratio = 24, 95% confidence interval = [23, 25] vs first-time callers), often hung up (49.1% vs first-time callers: 4.4%), and received 33.0% of the total counselling time. The yearly national call and caller rates were 893 calls and 212 unique callers per 100,000 inhabitants, respectively. CONCLUSIONS: Correcting for repeated unanswered calls provided an informative estimate of the response rate. The call distribution was highly skewed; a small group of daily callers accounted for most calls and were more likely to be answered. These callers frequently hung up before a conversation was initiated. National call rates facilitate cross-country comparisons.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".