Systematic Review of Research and Interventions With Frequent Callers to Suicide Prevention Helplines and Crisis Centers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: Background: Helplines worldwide have frequent callers who may occupy a large proportion of call volume. Therapeutic gain from frequent calling has been questioned. We conducted this review to identify the characteristics of frequent callers and to compile recommendations about how best to help them. Method: Using preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards, we searched for all empirical research in English and French from inception to May, 2020 in PubMed, PsycInfo, and the CRISE library. Results: We identified 738 manuscripts and retained 27 for analyses. Nine provided no definition of frequent callers; nine mixed frequent callers with repeat callers (>1 calls); nine concerned frequent callers (≥8 calls/month). The limited data suggest frequent callers are similar to other callers and often experience mental health problems, loneliness, and suicide risk. From recommendations in all 27 studies, we identified 10 suggestions to better manage and help frequent callers that merit validation. Limitations: The small number of empirical investigations and the diversity of their goals and methodologies limit generalizations. Although recommendations for helping callers may have face validity, empirical data on their effectiveness are scarce. Conclusion: Rather than focusing on reducing call frequency, we should empirically evaluate the benefits of interventions for frequent callers with different calling patterns, characteristics, and reasons for calling.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it