Perceptions of Contributions by International & Non-Governmental Organizations (IOs and NGOs) in the Health Care System of Buea & Limbe Municipalities, Cameroon
Bibliographic record
Abstract
Health is a very important part of any individual and the community as a whole. For this reason, many agencies both state and private as well as International Organizations (IOs) and Non-Governmental Organizations (NGOs) have been working the provision of health care services in Cameroon. This research evaluates the contribution of IOs and NGOs in the Health System of the Buea and Limbe Municipalities located in the South-West of Cameroon. The research hypothesizes that the perception of IOs and NGOs operating the Health System of the Buea and Limbe Municipalities has not improved the health system. The primary data for the study were obtained from field observations and 158 questionnaires were administered in the Buea and Limbe municipalities. Interviews were conducted on 20 health professional practitioners. Data collected were statistically analyzed and Chi-Square test was used to test the validity of the hypothesis. The results obtained show that IOs and NGOs have contributed in the health systems of the Buea and Limbe Municipalities by: educating these communities on health-related issues, subsidizing patient’s hospital bills, organizing vaccination campaigns and community dialogues, distributing mosquito nets, providing drugs, and sanitization campaigns. To limit the spread of Corona Virus (COVID-19), these NGOs and IOs provided face masks, soaps and hand sanitizers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".