Epidemiology and Determinants of Antirabies Vaccine Full‐Dose Completion Among Patients Attending the Nyagatare District Hospital, Rwanda: A Facility‐Based Cross‐Sectional Study
Bibliographic record
Abstract
Background and Aims Antirabies vaccine postexposure prophylaxis (PEP) is effective in preventing rabies when administered promptly and in full. This study assessed factors associated with antirabies PEP full‐dose completion among patients attending Nyagatare District Hospital. Methods A facility‐based cross‐sectional study was conducted using data from patients who sought antirabies PEP at the hospital’s emergency department in 2022. Patient files and registers were reviewed, and data were analyzed in STATA. Logistic regression was performed to identify factors associated with vaccine completion. Results Of the 472 participants, 50.0% were under 16 years, 58.9% were male, and 63.6% lived in rural areas. Most (90.9%) had health insurance, 51.7% received PEP during the dry season, 11.0% received the antitetanus vaccine, and 82.2% had WHO wound Category II. Only 26.5% completed the full vaccination schedule, 16.1% missed appointments, and 57.4% dropped out before completion. Health insurance significantly increased the odds of completing PEP (AOR = 2.19, p = 0.032). Age, sex, and wound characteristics were not significantly associated with ARV full‐dose completion. Conclusion Compliance with the full antirabies vaccine course was low, with only a quarter of patients completing all five doses. Improving completion requires targeted interventions such as reminder systems, community education, provider counseling, and financial support.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".