A Qualitative Study to Assess Challenges Faced During AEFI Surveillance
Bibliographic record
Abstract
Background: India has a functional and mature National Regulatory Authority which ensures that vaccines manufactured and used in the country are safe. Monitoring adverse events post-licensure is equally critical to identify infrequent adverse events due to the vaccine product. Material and Methods: This was a cross-sectional observational qualitative study done in a Metropolitan city in Maharashtra from January 2017 to June 2018. Focus group discussions (FGDs) were conducted with healthcare workers and stakeholders involved in adverse event following immunization (AEFI) surveillance in the city. The data obtained were transcribed and analyzed using the inductive method. Thematic analysis was done using the grounded theory. Data were analyzed using ATLAS ti 5.7.1. Results: It was found that both active and passive surveillance were being followed in the study area helped in improving reporting rates and in early detection and management of adverse events. It was noted that there was no proper training provided to doctors in the private sector. Reporting of adverse events depended upon a number of factors such as clinical seriousness, temporal proximity to vaccination and health care workers' awareness of and obligation to report particular adverse events, fear of blame, time pressures in completing a report, and confusion in whose responsibility it was to report. Conclusion: Mandatory training of all private practitioners conducting immunizations, CHVs to work in collaboration with private doctors for active surveillance of AEFI, and online reporting to be made available for easy reporting. Proper counseling of mothers regarding giving paracetamol to the vaccine beneficiaries. Greater convergence is required between national regulators, and vaccine pharmacovigilance stakeholders including Central drugs standard control organisation (CDSCO), Pharmacovigilance programme of India (PvPI), and AEFI surveillance program, especially at the city and state level is required to handle vaccine safety issues at various levels in a faster and more effective manner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".