Evidence map›Paper›PMID 39629428›Full record

ArticleJournal of family medicine and primary care2024

Post-COVID pandemic: The new normal and aftermath.

Keerthi Panneer Selvam, Kalpana Kosalram, Saravanan Chinnaiyan

Abstract read
In one paragraph

Article in Journal of family medicine and primary care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Keerthi Panneer SelvamSRM School of Public Health, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India.
Kalpana KosalramSRM School of Public Health, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India.
Saravanan ChinnaiyanSRM School of Public Health, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The COVID-19 pandemic has brought about unparalleled worldwide transformations, impacting all facets of human existence, including health systems, economies, societal norms, and individual behaviors. Aim: The goal is to comprehend the enduring alterations in public health strategies, economic recuperation processes, changes in work and education paradigms, and the psychological consequences for populations globally. Methods and Materials: This analysis uses a multidisciplinary approach by incorporating data from healthcare studies, economic reports, educational research, and psychological assessments. It aims to offer a comprehensive perspective on the world after the pandemic. Results: The findings suggest that the "New Normal" encompasses a wide range of changes, such as a greater dependence on digital technology, a transition toward remote work and learning, substantial modifications in global supply chains, and a revised outlook on health and wellness. The potential long-term consequences of these changes indicate that some may provide opportunities for innovation and expansion, whereas others present challenges that necessitate strategic planning and policy interventions. Conclusion: The aftermath of the COVID-19 pandemic presents a critical moment for global leaders, policymakers, and individuals to navigate the complexities of a transformed world, emphasizing the need for resilience, adaptability, and a renewed commitment to collective well-being.

Indexed as

COVID-19global crisisimpacts on healthpandemicsocial and economic impacts

Identifiers

PMID39629428
PMCPMC11610867

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.