The perception of pre- and post-natal marijuana exposure on health outcomes: A content analysis of Twitter messages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The prevalence of marijuana use during pregnancy ranges from 3-30% , and most of this is for recreational purposes. Marijuana exposure during pregnancy has been linked with low birth weight babies and other adverse child health outcomes. Twitter is a popular news and social networking outlet, and is frequently used to access information about population health and behavior. The primary objective of this study was to investigate the types of messages disseminated on Twitter about marijuana use and infant and maternal health. The secondary objective was to describe the reported health outcomes associated with prenatal and postnatal marijuana use. Tweets were collected from the inception of Twitter (2006) until April 2017. If tweets included links, these links were examined to investigate the source of the message and to clarify the user's intent. In total, 550 tweets were captured, with most tweets (77.6%) having a neutral tweet tone, suggesting uncertainty about the health effects associated with pre- and post-natal marijuana exposure. The sources attached to the original tweets, however, were more likely to report on negative health outcomes. The most common health outcomes associated with prenatal marijuana exposure were: poor brain development (27.3%), inadequate development of the nervous system (23.6%), low birth weight (23.3%), poor behavioral outcomes (21.0%), and infant memory issues (19.3%). The inverse association between marijuana use and the quality and quantity of milk produced by the mother was the most commonly reported tweet for the lactation period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it