“USANDO CONDÓNâ€: UNA INTERVENCIÓN CONDUCTUAL CUASI-EXPERIMENTAL BASADA EN TEORÃA PARA MEJORAR LA AUTOEFICACIA PERCIBIDA DEL USO DEL CONDÓN EN ADOLESCENTES MEXICANOS
Bibliographic record
Abstract
Aim: Sexually transmitted infections and pregnancy are public health problems in adolescence, preventable with the consistent condom use. This article presents the preliminary efficacy of “Usando Condón”, a theory-based beÂhavioral intervention to increase the perceived self-efficacy for condom use. Method: “Usando Condón” consisted in two 90-minute sessions targeting Mexican adolescents aged 15-19 years. The sampling method was divided into three phases: a) randomization of high schools; b) randomization of selected high schools; c) non-probabilistic partiÂcipants sampling. Three hundred ninety-two adolescents were included and distributed in three groups: experimenÂtal group experimental-group (n = 132), control-pamphlet group (n = 130) and control-control group (n = 130). The intervention was measured with the Self-efficacy Scale for Condom Use among Mexican Adolescents. Inferential statistics were used. Results: The groups were equivalent (sociodemographic variables), except for age (p > .05). Significant statistical difference between controls and experimental groups was found at the re-test (p < .05, F = 18.089, CI 95%). Conclusion: “Usando Condón” increased the perceived self-efficacy for condom use levels among adolescents. The intervention needs to be further tested in different contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".