The effectiveness of helplines for the treatment of alcohol and illicit substance use
Bibliographic record
Abstract
While tobacco helplines (quitlines) are thought to be effective, helplines which treat other substance use do not have an established evidence base. A review was conducted of the literature on illicit drug or alcohol (IDA) helplines. The literature search was conducted in five databases. Studies prior to 2014 were included if published in English, and involved the use of a telephone counselling helpline for the treatment of illicit drug or alcohol use. Review papers, opinion pieces, letters or editorials, case studies, published abstracts and posters were excluded. Initial searching identified 2178 articles and after removing duplicates and those meeting the exclusion criteria, there were 36 publications for review. A total of 29 articles provided descriptive information about 19 different IDA helplines which operated in the US (42%), Europe (21%), Australia (21%), Asia (11%) and Canada (5%). These services reported monthly call rates from 3.7 to over 23,000 calls per month. A total of nine articles provided evaluative information on eight different IDA helplines: four articles included a comparison of treatment outcomes against a control group and five articles included information on treatment satisfaction or service utilisation. Together they provide some evidence that these services are effective. Although there was little consistency in the measures used between articles which assessed helpline satisfaction, all but one reported high satisfaction. Although the evidence is mainly supportive of IDA helplines, further work is required to compare treatment outcomes in randomized groups.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".