Pilot study of a repeated random sampling method for surveys focusing on date-specific differences in alcohol consumption among university students
Bibliographic record
Abstract
BACKGROUND: This paper proposes and pilots a repeated random sampling method to promote the likelihood of collecting drinking data equally representative of the behavior of university students at all times through the academic year. METHODS: From October, 2016, to May, 2017, random samples of 1350 students were selected from the 39,155 undergraduate students enrolled in the fall semester at University of Houston. These students were sent an email inviting them to complete an online survey (entered into a weekly draw for a $50 gift certificate if responded). RESULTS: The response rate was low (6%). Among participants who reported drinking in the last week, there was a variation as expected in the amount of drinking observed depending on the time of year (e.g., during exams). CONCLUSIONS: While the sampling methods show promise, procedures would need to be implemented to substantially increase response rates before the proposed methods could be seen as an advantage over existing survey sampling procedures.
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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 | 0.047 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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; a candidate call from one teacher head, 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".