WhatsApp and Wellness: Exploring the Motivations and Challenges in Health Literacy and Health Information Seeking Among African Immigrants
Bibliographic record
Abstract
Purpose: The purpose of this study is to investigate health information seeking behavior of African Immigrants in Vancouver, British Columbia, Canada. Methodology: Survey research design was employed in this study. Web-based questionnaire was used to collect data from 32 participants who are members of a WhatsApp Group for African Immigrants in Vancouver BC. Descriptive statistics of simple percentage was used to analyze the data. Findings: The study examined motivation for using WhatsApp as a source of health information seeking, familiarity with and affordability of healthcare plan in Vancouver BC, and the healthcare access challenges of African immigrants. While most respondents reported familiarity with the healthcare system in Vancouver, BC, only a few of them had a family physician, indicating a gap between perceived knowledge and practical engagement with healthcare professionals. Many participants relied on WhatsApp for health information; however, such platforms often provide only temporary solutions, underscoring the importance of professional healthcare consultations. Limitations: The study’s participants were selected through purposive sampling which cannot be representative of the population as a whole. Also, using a Web-based questionnaire as a data collection instrument and focusing on the participants who are members of a Whatsapp Group restricted the size of the sample. Originality: This study contributes to the ongoing discourse on the use of Social Media beyond mere social interaction. It also creates a call-to-action for healthcare stakeholders in multicultural societies on the need to rethink culturally-appropriate healthcare access.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".