Community based Empirical study and Statistical Analysis about RH Cases with Canadian International Development Agency and Planned Parenthood Federation of Canada
Bibliographic record
Abstract
CBRHEP is an initiative of a package of service delivery at the community doorstep to meet the unmet needs and avoid the challenges of access to services and mobility of women, in particular. However it was observed that at certain locations, despite the service provision at a nominal cost, the preventive methodology acceptance is very low. The main source of this information is the reports received from the Project Locations regarding the preventive methodology use and clientage of RH services. The main question of the present study is to find out that, what are the factors that restrain people from availing the services, especially preventive methodology, while they are being provided at a nominal cost, or sometimes free of cost, at the community doorstep? This query can be answered by analyzing the views of the community members at the project locations from different angles, like socio-cultural, religious, economic, medical etc. It is also a very interesting inquiry regarding the operation of the project and existing gaps in its implementation. The low acceptance of the preventive methodology has many dimensions and determinants; the discrepancy between stated fecundity preferences and reported preventive methodology behavior is often interpreted as indicative of latent demand for preventive methodology. Indeed, surveys carried out in many developing countries have shown that a discrepancy between productiveness preferences and behavior, commonly labeled “Unmet Need for FP,” characterizes a sizable fraction of women of reproductive age in the developing societies. However the gap between the desired lushness and actual use not necessarily because of this unmet need; the social factors may be determinant of many behaviors and preventive methodology is one of them. According to Sathar et al (2001) “…what is remarkable about Pakistan is not the existence of this preference–use gap but rather its persistence at relatively high levels for decades without any significant change in prevalence and, accordingly, in period fecundity rates, which in the three decades leading up to the 1990s probably exceeded six births per woman.”
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".