The Inclusion of Husbands in Family Planning an Explanatory Study in Kwabre East
Bibliographic record
Abstract
Over the years; from 1800 to 2011, the world population, as estimated by the United States Census Bureau recorded 6.999 billion with a growing rate of 1.1% or more per annum. In relation to the global rapid growth rate, international family planning initiatives bodies have come out with policies to target the minimization of the high fertility rates triggering the rapid population growth rate in both the developed and developing world. During this time, the development of modern contraception, family planning programmes, and fertility surveys were mainly focused on the female population. Therefore, the study seeks to assess the knowledge and perception of men on contraceptive use. Cross-sectional data were sourced across the inhabitants of the Kwabre East District for the analysis of the problem stated above on contraceptive used with a sample size of 300 marriage couples. The simple random sampling technique was used to select the community for the study and purposive sampling was used to select the respondents to the survey. The research used both qualitative and quantitative approach for data analysis in the study. The study used logit regression for the statistical analysis and presentation of the data gathered. The study revealed that about 90.0% of the respondents, representing the majority, have ever been heard of contraceptive use. On the part of the perception of men on contraceptive use, respondents gave positive responses regarding contraceptive use. The study also revealed a positive correlation between the educational status of the respondents and the use of contraceptive. The study strongly, recommends that the health workers should intensify the education on contraceptive use especially to men in order to correct the misconceptions (i.e. most males were naive about the use of contraceptives) in order to widen the knowledge base of the males on the use of contraceptive and reproductive decision making. In order to promote male involvement in contraceptive use, which is also the wish of most of the respondents, it is recommended that just like Mother-to-Mother Support Groups are there to promote activities of women, Father-to-Father Support Groups should also be formed to involve men in educating themselves on contraceptive use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".