Outcomes of two randomized controlled trials, employing participants recruited through Mechanical Turk, of Internet interventions targeting unhealthy alcohol use
Bibliographic record
Abstract
BACKGROUND: Two randomized controlled trials (RCTs) were conducted to explore the utility of the Mechanical Turk (MTurk) crowdsourcing platform to conduct rapid trials evaluating online interventions for unhealthy alcohol use. METHODS: Both trials employed a staged recruitment procedure where participants who drank in an unhealthy fashion were identified using a baseline survey and then invited to take part in a 6-month follow-up. Participants in both trials were randomized to receive one of several different online interventions or to a no intervention control condition. In study 1, the online interventions were password protected and only those who accessed the study portal were randomized to condition. In study 2, participants were directed to free-of charge interventions and asked to send a screenshot of the intervention to demonstrate that they had complied. RESULTS: Participants reporting unhealthy alcohol use were recruited fairly rapidly. Large numbers of screeners were completed (Study 1: n = 4910; Study 2: n = 5812), found eligible (Study 1: n = 3741; Study 2: n = 4095), and randomized to condition (Study 1: n = 511; Study 2: n = 878). Fair follow-up rates were observed at 6 months for each study (Study 1: 82%; Study 2: 66%). Neither trial was able to clearly demonstrate that providing access to the online interventions lead to increased reductions in alcohol use as compared to the control group. CONCLUSIONS: While recruitment through a crowdsourcing platform is rapid and relatively low cost, it is possible that the lack of impact of the online websites employed in these trials could be due to the source of participants rather than the lack of efficacy of the interventions. TRIAL REGISTRATION: ClinicalTrials.gov # NCT02977026 and NCT03060135 .
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How this classification was reachedexpand
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 | 0.259 | 0.589 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".