Factors Contributing to Pregnancies among Tertiary Students at the University of Namibia
Bibliographic record
Abstract
The Government of Republic of Namibia through the services rendered by the Ministry of Health and Social Services (MoHSS) continues to provide various free health services including contraceptives to its citizens. Nevertheless, several challenges related to unplanned pregnancies among tertiary student’s remains a challenge, which includes poor reproductive health status, socio-economic consequences rapid-population growth, rural-urban migration of youths accompanied by proliferation of informal settlements around cities, high youth unemployment and crime. This requires institutions of higher learning to establish which strategies are likely to address these problems of unplanned pregnancies among tertiary students. The aim of this study was to explore and describe the contributing factors to pregnancies amongst tertiary students at a selected satellite campus in order to make recommendations to the University of Namibia (UNAM). An exploratory, descriptive and qualitative design was used. The study was contextual in nature. A convenient sampling was used. The data were collected through three focus-group discussions with 19 students from the three faculties namely: Education, Health Sciences and Management Sciences. Data were analyzed through qualitative content analysis. Strategies to ensure trustworthiness and ethical implementation of the study were implemented. It became evident from the study findings that factors which are contributing to pregnancies, as evidenced by the four emerged themes namely: Personal factors, institutional related factors and improvements measures. This study has implications for higher education institutions in terms of promoting sexual and reproductive health information and increasing access to a range of contraceptive methods of campus which are key in the prevention of pregnancies among tertiary students. Participants in this study recommended that peer educators and students counsellor within the campus should be used as a vehicle to provide support and guidance to students on reproductive health choices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".