Challenges Experienced by Health Care Workers During Service Delivery in the Geographically Challenging Terrains of North-East India: Study Involving a Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: The public health landscape in North-East India is marked by the foundational principle of equitable health care provision, a critical endeavor considering the region's intricate geography and proximity to international borders. Health care workers grapple with challenges, such as treacherous routes, limited infrastructure, and diverse cultural nuances, when delivering essential medical services. Despite improvements since the National Rural Health Mission in 2005, challenges persist, prompting a study to identify health care workers' challenges and alternative strategies in Manipur and Nagaland. OBJECTIVE: This study aims to document the challenges experienced by health care workers during service delivery in the geographically challenging terrains of North-East India. METHODS: This study is part of the i-DRONE (Indian Council of Medical Research's Drone Response and Outreach for North East) project, which aims to assess the feasibility of drone-mediated vaccine and medical delivery. This study addresses the secondary objective of the i-DRONE project. In-depth interviews of 29 health care workers were conducted using semistructured questionnaires in 5 districts (Mokokchung and Tuensang in Nagaland, and Imphal West, Bishnupur, and Churachandpur in Manipur). Nineteen health facilities, including primary health care centers, community health centers, and district hospitals, were selected. The study considered all levels of health care professionals who were in active employment for the past 6 months without a significant vacation and those who were engaged in ground-level implementation, policy, and maintenance activities. Data were recorded, transcribed, and translated, and subsequently, codes, themes, and subthemes were developed using NVivo 14 (QSR International) for thematic analysis. RESULTS: Five themes were generated from the data: (1) general challenges (challenges due to being an international borderline district, human resource constraints, logistical challenges for medical supply, infrastructural issues, and transportation challenges); (2) challenges during the COVID-19 pandemic (increased workload, lack of diagnostic centers, mental health challenges and family issues, routine health care facilities affected, stigma and fear of infection, and vaccine hesitancy and misinformation); (3) perception and awareness regarding COVID-19 vaccination; (4) alternative actions or strategies adopted by health care workers to address the challenges; and (5) suggestions provided by health care workers. Health care workers demonstrated adaptability by overcoming these challenges and provided suggestions for addressing these challenges in the future. CONCLUSIONS: Health care workers in Manipur and Nagaland have shown remarkable resilience in the face of numerous challenges exacerbated by the pandemic. Despite infrastructural limitations, communication barriers, and inadequate medical supply distribution in remote areas, they have demonstrated adaptability through innovative solutions like efficient data management, vaccination awareness campaigns, and leveraging technology for improved care delivery. The findings are pertinent for not only health care practitioners and policymakers but also the broader scientific and public health communities. However, the findings may have limited generalizability beyond Manipur and Nagaland.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".