Hand Therapy Assessments for Use with International Technicians (HTAIT)
Bibliographic record
Abstract
# Background To determine whether aggregated searches for pregnancy prevention or pregnancy termination predicts US State teenage birth rates. # Methods US birth rate data for the 50 states, and search engine query data (Google Trends) for "condom" and "abortion" were used in an ecological analysis. Multivariable ordinary least squares regression was used to predict state-level birth rates from state-level searches for condom and abortion. # Results The final model accounted for 35% of the variance (R^2^=0.347). Abortion and condom had similar, absolute, standardized parameters (β≈0.5). High state-levels of searches for abortion were associated with higher teenage birth rates, whereas high state-levels of searches for condom were associated with lower teenage birth rates. # Conclusions Google Trends data for abortion and condom can be used to model US state-levels teenage birth rates. This raises the possibility of well targeted, accessible and relevant information for populations wanting to avoid unwanted pregnancies.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.013 |
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".