Suicide Prevention and New Technologies: Towards Evidence Based Practice
Bibliographic record
Abstract
Throughout most of human history people with personal problems would need to seek out another person to obtain help or emotional support. The alternative was to deal with the problem oneself, pray for divine intervention or have some solace from religious beliefs. In more recent times, for those few with the ability and culture to do so, one could also seek information, guidance or support from printed books. The second half of the 20th century was a period when the use of face- to-face professional help expanded throughout the world. During this same period, books became a source of a “do-it-yourself” psychological treatment, with an exponential growth in self-help books for almost any human affliction. In the mid-20th century, a new technology, the telephone, expanded the options for help seeking. Telephone support for suicidal people expanded rapidly since the start of the Samaritan movement in the United Kingdom, founded by Reverend Chad Varah in 1953 (Mishara, 2012). Today, telephone helplines provide crisis inter- vention, emotional support and suicide prevention services throughout the world. For examples, Befrienders Worldwide has affiliate helplines in more than 40 countries that provide telephone help based upon the Samaritan approach. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.137 | 0.254 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".