Effects of COVID-19 Illness and Vaccination Infodemic Through Mobile Health, Social Media, and Electronic Media on the Attitudes of Caregivers and Health Care Providers in Pakistan: Qualitative Exploratory Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had a significant impact on different countries because of which various health and safety measures were implemented, with digital media playing a pivotal role. However, digital media also pose significant concerns such as misinformation and lack of direction. OBJECTIVE: We aimed to explore the effects of COVID-19-related infodemics through digital, social, and electronic media on the vaccine-related attitudes of caregivers and health care providers in Pakistan. METHODS: This study employs a qualitative exploratory study design with purposive sampling strategies, and it was conducted at 3 primary health care facilities in the province of Sindh, Pakistan. Seven focus group discussions with health care providers and 60 in-depth interviews with caregivers were conducted using semistructured interviews through virtual platforms (ConnectOnCall and Zoom). Transcripts were analyzed through thematic analysis. RESULTS: Our study reveals the pivotal role of electronic media, mobile health (mHealth), and social media during the COVID-19 pandemic. Four major themes were identified: (1) sources of information on COVID-19 and its vaccination, (2) electronic media value and misleading communication, (3) mHealth leveraging and limitations during COVID-19, and (4) social media influence and barriers during COVID-19. Health care providers and caregivers reported that the common sources of information were electronic media and mHealth, followed by social media. Some participants also used global media for more reliable information related to COVID-19. mHealth solutions such as public awareness messages, videos, call ringtones, and helplines promoted COVID-19 prevention techniques and vaccine registration. However, the overwhelming influx of news and sociobehavioral narratives, including misinformation/disinformation through social media such as WhatsApp, Facebook, and Twitter, were found to be the primary enablers of vaccine-related infodemics. Electronic media and mHealth were utilized more widely to promote information and communication on the COVID-19 pandemic and vaccination. However, social media and electronic media-driven infodemics were identified as the major factors for misinformation related to COVID-19 and vaccine hesitancy. Further, we found a digital divide between the urban and rural populations, with the use of electronic media in rural settings and social media in urban settings. CONCLUSIONS: In a resource-constrained setting like Pakistan, the usage of mHealth, social media, and electronic media for information spread (both factual and mis/disinformation) related to COVID-19 and its vaccination had a significant impact on attitudes toward COVID-19 vaccination. Based on the qualitative findings, we generated a model of digital communications and information dissemination to increase knowledge about COVID-19 and its prevention measures, including vaccination, which can be replicated in similar settings for other disease burdens and related infodemics. Further, to mitigate the infodemics, both digital and nondigital interventions are needed at a larger scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".