ArticleScientific reports2024
Longitudinal qualitative study on the psychological experiences of COVID-19 patients based on timing it right framework.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Needs of Acute Respiratory Failure Survivors Throughout 1 Year of Recovery: A Qualitative Analysis Using the Timing It Right Framework.CHEST critical care · 2025Article
- Understanding challenges in healthcare access: qualitative insights from healthcare providers and people living in socio-economically vulnerable circumstances.International journal for equity in health · 2025Article
- Research on safety strategies for nucleic acid testing in sudden epidemic conditions based on a SEIARD dynamic model.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Timing it right framework was used as a framework to explore the illness experiences of patients infected with COVID-19 and to analyze the patients' perceptions of the disease and their true inner feelings to provide a reference for the control of infectious diseases. This research adopted a phenomenological research approach to develop a longitudinal qualitative study. A purposive sampling method was used to select participants and 37 patients were recruited. Depending on the principle that participants should have maximum variation and sampling should cease when interviews content saturation is achieved, 16 COVID-19 patients in an isolation ward in Ningbo City, Zhejiang Province were finally included. Data were collected using semi-structured interviews, and the content of the interviews was analyzed by Colaizzi's 7-step method. The themes of COVID-19 patients' experiences at various phase were presented as follows: multiple emotions intertwined at the time of diagnosis (anxiety, stressful panic, facing the diagnosis calmly), multiple pressures during the hospitalization period (concerns about the disease, unable to adapt to the ward environment, worrying about future hardship), growth of positive illness experience during the isolation and observation period (sublimated outlook on life, affirmation of the government's anti-epidemic policy, more concerned about their own health), adjustment after returning to society (stigma, loss of previous living environment, problems caused by nucleic acid testing), and adaptation to social life (return to normal life, avoidance of illness experience, post-covid-19 syndrome). The illness experience of COVID-19 patients changed dynamically with time, but a sense of shame and uncertainty about recovery was present throughout the process. Interventions should be developed according to the needs of the patients at different times to inform subsequent optimization of care and management of infectious diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.