Pre-/Post- Assessment of a Sexual and Reproductive Health Training Program for Young People in Namibia
Bibliographic record
Abstract
During the first COVID-19 lockdown in Namibia (March-September 2020), the Ministry of Health and Social Services reported there were an estimated 14,983 teenage pregnancies in 2020, an increase from the prior year’s estimated 13,552. The regions of Kavango East and West were particularly impacted. In response to these figures, the Ombetja Yehinga Organisation (OYO), a youth-focused Namibian non-governmental organization, facilitated an after-school intervention in 2021 to discuss key sexual and reproductive health knowledge. An identical questionnaire was administered at both pre- and post-test, in order to provide baseline information for assessing the effectiveness of a school-based intervention to promote safe sexual behaviours. A total of 18 schools in the regions of Kavango East and West participated in the intervention between May-September 2021, and 638 learners aged 13-25 were included in data analysis after completing both the pre- and post-tests. Prior to the intervention, knowledge on sexual and reproductive health, including safe sexual behaviours and accessing contraceptives was limited. Results obtained at post-test indicate there were significant increases in participants’ level of knowledge between pre- and post-test, suggesting that school-based interventions (such as the OYO program) may be effective in disseminating this crucial information to at-risk populations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".