Perceptions of Mothers and Caregivers Regarding the Factors Affecting Low Uptake of Measles Immunisation Among Children Under 5 Years in Nyangana District, Namibia
Bibliographic record
Abstract
Immunisation is considered to be amongst the most successful and cost-effective disease prevention interventions available. The aim of this study was, to investigate the perceptions of mothers/caregivers on the factors that impact on the uptake of measles immunisation in the Nyangana Health District, with a view to improving measles immunisation coverage. A qualitative exploratory study design was used to collect data from the study participants by the use of interviews. Data was audio-taped and transcribed verbatim. The recorded interviews were translated from the Gciriku language to English. Data was analysed through the use of the Thematic Content Analysis approach. Mothers whose children were vaccinated and those whose children were not vaccinated had both positive perceptions about immunisation. The findings indicates that, despite awareness and willingness for some mothers to bring their children for subsequent follow-up vaccinations, barriers such as inability to pay for transport, lack of support for single mothers and absence of support structures was hindering factors to immunisation uptake. Additionally, health system factors such as health care provider attitudes, staff shortages, inconvenient immunisation services, time constraints, inadequate outreach services and lack of tracking services for children who missed their measles immunisation were found to have an influence on the immunisation uptake. The children who missed their immunisation doses were mostly for single mothers and for those mothers who stayed far away from the clinic. The study concludes that the relationship between health care providers and mothers/caregivers and support from other social structures, should be good, in order to motivate mothers to use immunisation services. The study recommends that the following aspects be addressed, as they have the potential to improve the low uptake of measles immunisation: patient/provider relationship, information sharing, and supervision in the health facility, access to services, availability of outreach services, improved data tracking and active involvement of all stakeholders. These children should be targeted through improved outreach services and the use of health extension workers for contact tracing of children who missed their immunisation.
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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 | 0.000 |
| 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) | 0.002 | 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".