Bibliographic record
Abstract
Although the US adolescent pregnancy rate is at a historic low,1 it remains significantly higher than European coun-tries and Canada. Increased utilization of user independent contraceptives such as the intrauterine device (IUD) can decrease adolescent pregnancy rates.2 IUDs are safe and effective for adolescents and nulliparous women. Professional guidelines support IUDs as a first-line contra-ceptive for adolescents,3-5 yet only approximately 4 % of contracepting US adolescents use IUDs.6 Adolescents ’ low use of IUDs is multifactorial and is partly because of pri-mary care physicians ’ (PCPs) lack of counseling about or offering IUDs.7,8 Contraception counseling in general involves discussion of multiple effective, appropriate options. Traditionally, PCPs took a paternalistic counseling approach, which emphasized provider-directed decision making. When using this type of clinical approach, PCPs determined which contraceptive would meet the patient’s best interest and the patient was offered limited option. Currently, there is empha-sis on patient-centered counseling with shared patient– provider decision making, resulting in greater patient autonomy and choice over selection of contraceptive method. In this research letter, we describe PCPs ’ approaches to contraception counseling with adolescents, specifically focusing on their views about appropriate IUD candidates. These data were collected as part of a larger study exploring
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.194 | 0.047 |
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".