Provision of Health Services to the Internally Displaced Persons in Maiduguri, Borno State, Nigeria: Collaborative Approach
Bibliographic record
Abstract
There is need to respond to the plight of the Internally Displaced Persons (IDPs) amidst the growing number of calls for concerted efforts and better management. This can be facilitated through collaboration among the agencies responsible for the management of IDPs. The government of Nigeria and indeed Borno state government lacked the capacity to wholly manage the IDPs, hence, the need for the NGOs to assist the government in that regard. The study assesses the effect of collaboration among agencies in the management of the IDPs in Borno state. The obligatory humanistic theory was used in the study. The study adopted survey method and both primary and secondary data were used. The questionnaire, Interview, and Focus Group Discussion were used to obtained primary data. The study population is 2018 consisting of government officials, NGOs, and IDPs. The sample size of the study was 349 respondents; 333 government officials and 16 NGOs. Multi-staged sampling technique was used in selecting the sample. Both descriptive and inferential statistics were used for analysis of the data obtained. ANOVA and chi-square were used to test the hypotheses. The study found out that inter-agency collaboration effort has significantly reduced the outbreak of disease in IDPs camps in Maiduguri. The IDPs have access to child and maternal cares services and all barriers to accessing quality healthcare services have been eliminated in camps in Maiduguri. The study concludes that inter-agency collaboration has been effective in the provision of healthcare services to IDPs in Borno state. The study, therefore, recommends among others that agencies should work out modality to ensure improved referral healthcare system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".