Primary Health Care Facility Preparedness for Outpatient Service Provision During the COVID-19 Pandemic in India: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Primary health centers (PHCs) represent the first tier of the Indian health care system, providing a range of essential outpatient services to people living in the rural, suburban, and hard-to-reach areas. Diversion of health care resources for containing the coronavirus disease (COVID-19) pandemic has significantly undermined the accessibility and availability of essential health services. Under these circumstances, the preparedness of PHCs in providing safe patient-centered care and meeting the current health needs of the population while preventing further transmission of the severe acute respiratory syndrome coronavirus 2 infection is crucial. OBJECTIVE: The aim of this study was to determine the primary health care facility preparedness toward the provision of safe outpatient services during the COVID-19 pandemic in India. METHODS: We conducted a cross-sectional study among supervisors and managers of primary health care facilities attached to medical colleges and institutions in India. A list of 60 faculties involved in the management and supervision of PHCs affiliated with the community medicine departments of medical colleges and institutes across India was compiled from an accessible private organization member database. We collected the data through a rapid survey from April 24 to 30, 2020, using a Google Forms online digital questionnaire that evaluated preparedness parameters based on self-assessment by the participants. The preparedness domains assessed were infrastructure availability, health worker safety, and patient care. RESULTS: A total of 51 faculties responded to the survey. Each medical college and institution had on average a total of 2.94 (SD 1.7) PHCs under its jurisdiction. Infrastructural and infection control deficits at the PHC were reported in terms of limited physical space and queuing capacity, lack of separate entry and exit gates (n=25, 49%), inadequate ventilation (n=29, 57%), and negligible airborne infection control measures (n=38, 75.5%). N95 masks were available at 26 (50.9%) sites. Infection prevention and control measures were also suboptimal with inadequate facilities for handwashing and hand hygiene reported in 23.5% (n=12) and 27.4% (n=14) of sites, respectively. The operation of outpatient services, particularly related to maternal and child health, was significantly disrupted (P<.001) during the COVID-19 pandemic. CONCLUSIONS: Existing PHC facilities in India providing outpatient services are constrained in their functioning during the COVID-19 pandemic due to weak infrastructure contributing to suboptimal patient safety and infection control measures. Furthermore, there is a need for effective planning, communication, and coordination between the centralized health policy makers and health managers working at primary health care facilities to ensure overall preparedness during public health emergencies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".