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Record W4399097128 · doi:10.2196/47520

COVID-19 Resilience and Risk Reduction Intervention in Rural Populations of Western India: Retrospective Evaluation

2024· article· en· W4399097128 on OpenAlexvenueno aff
Saurav Basu, Meghana Desai, Anup Karan, Surbhi Bhardwaj, Himanshu Negandhi, Nitin Jadhav, Amar Maske, Sanjay Zodpey

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthPandemicEnvironmental healthHygienePsychological interventionPsychological resilienceIntervention (counseling)MedicineEconomic growthCoronavirus disease 2019 (COVID-19)SocioeconomicsNursingPsychologySociologyDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Globally, especially in the low- and middle-income countries (LMICs), rural populations were more susceptible to the negative impact of the COVID-19 pandemic due to lower levels of community awareness, poor hygiene, and health literacy accompanying pre-existing weak public health systems. Consequently, various community-based interventions were engineered in rural regions worldwide to mitigate the COVID-19 pandemic by empowering people to mount both individual and collective public health responses against the pandemic. However, to date, there is paucity of information on the effectiveness of any large-scale community intervention in controlling and mitigating the effects of COVID-19, especially from the perspective of LMICs. OBJECTIVE: This retrospective impact evaluation study was conducted to evaluate the effect of a large-scale rural community-based intervention, the COVID-Free Village Program (CFVP), on COVID-19 resilience and control in rural populations in Maharashtra, India. METHODS: The intervention site was the rural areas of the Pune district where CFVP was implemented from August 2021 to February 2022, while the adjoining district, Satara, represented the control district where the COVID-Free Village Scheme was implemented. Data were collected during April-May 2022 from 3500 sample households in villages across intervention and comparison arms by using the 2-stage stratified random sampling through face-to-face interviews followed by developing a matched sample using propensity score matching methods. RESULTS: The participants in Pune had a significantly higher combined COVID-19 awareness index by 0.43 (95% CI 0.29-0.58) points than those in Satara. Furthermore, the adherence to COVID-appropriate behaviors, including handwashing, was 23% (95% CI 3%-45%) and masking was 17% (0%-38%) higher in Pune compared to those in Satara. The probability of perception of COVID as a serious illness in patients with heart disease was 22% (95% CI 1.036-1.439) higher in Pune compared to that in Satara. The awareness index of COVID-19 variants and preventive measures were also higher in Pune by 0.88 (95% CI 0.674-1.089) points. In the subgroup analysis, when the highest household educational level was restricted to middle school, the awareness about the COVID-control program was 0.69 (95% CI 0.36-1.021) points higher in Pune, while the awareness index of COVID-19 variants and preventive measures was higher by 0.45 (95% CI 0.236-0.671) points. We did not observe any significant changes in the overall COVID-19 vaccination coverage due to CFVP implementation. Furthermore, the number of COVID-19 deaths in both the sampled populations were very low. The probability of observing COVID-19-related stigma or discrimination in Pune was 68% (95% CI 0.133-0.191) lower than that in Satara. CONCLUSIONS: CFVP contributed to improved awareness and sustainability of COVID-appropriate behaviors in a large population although there was no evidence of higher COVID-19 vaccination coverage or reduction in mortality, signifying potential applicability in future pandemic preparedness, especially in resource-constrained settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.083
GPT teacher head0.468
Teacher spread0.385 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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