Can Amazon's Mechanical Turk be used to recruit participants for internet intervention trials? A pilot study involving a randomized controlled trial of a brief online intervention for hazardous alcohol use
Why this work is in the frame
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Bibliographic record
Abstract
To determine whether Amazon's Mechanical Turk (MTurk) might be a viable means of recruiting participants for online intervention research. This was accomplished by conducting a randomized controlled trial of a previously validated intervention with participants recruited through MTurk. Participants were recruited to complete an online survey about their alcohol use through the MTurk platform. Those who met eligibility criterion for age and problem drinking were invited to complete a 3-month follow-up. Those who agreed were randomized to receive access to an online brief intervention for drinking or were assigned to a no intervention control group (i.e., thanked and told that they would be re-contacted in 3 months). A total of 423 participants were recruited, of which 85% were followed-up at 3-months. All participants were recruited in 3.2 h. Only 1/3 of participants asked to access the online brief intervention did so. Of the 4 outcome variables (number of drinks in a typical week, highest number on one occasion, number of consequences, AUDIT consumption subscale), one displayed a significant difference between conditions. Participants in the intervention group reported a greater reduction between on the AUDIT consumption subscale between baseline and 3-month follow-up compared to those in the no intervention control group (p = 0.004). Despite the current pilot showing only limited evidence of impact of the intervention among participants recruited through MTurk, there is potential for conducting trials employing this population (particularly if methods are employed to make sure that participants receive the intervention). This potential is important as it could allow for the rapid conduct of multiple trials during the development stages of online interventions.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 it