WhatsApp In Health Communication: \nThe Case Of Eye Health In Deprived \nSettings In India
Bibliographic record
Abstract
The aim of this study was to explore the use of WhatsApp in developing a \ncommunity based practice of eye health promotion in a deprived locality bordering \na metropolitan city in India. \nGlobally, 285 million people are visually impaired, a quarter of whom live in India, \nwhich results in lower employment and lessened productivity. The national \nblindness prevention strategy aims at eyecare promotion through health behaviour \nchange achieved by raising awareness. Traditionally, health behaviour change has \nbeen achieved through conventional communication platforms like radio and \ntelevision-. The recent exponential development in social media technology, \nubiquitous and inexpensive, offers significant potential for two-way communication \nin real time with a wider audience, including those from disadvantaged groups. \nWhatsApp, an inexpensive social media platform which is widely used in the Indian \nsubcontinent, may offer an important channel for eyecare related health \ncommunication. Importantly, no study has systematically evaluated WhatsApp in \npromoting health communication on eye care in India, specifically in its largely \ndeprived population. \nThis qualitative study used WhatsApp (as an interventional tool) to create an \ninformation resource link on basic eye care between a tertiary city based healthcare \nprovider and the deprived community, resident in the fringe of the city. WhatsApp \nuse was facilitated by specially selected local women trained in information usage \nto disseminate contextual audio-visual information on eye care through multiple \n‘educational’ sessions. Perspectives of 10 healthcare providers, 10 community \nhealth advocates and 30 women participants from the deprived community, were \nqualitatively explored. Changes in health behaviour of the deprived community \nmembers were also assessed. A thematic analysis was performed to systematically \ninterrogate data to create meaningful themes. \nThis study confirmed the presence of a significant information gap on eye care on \nthe face of high disease burden. The use of WhatsApp was supported unanimously \nv \nby healthcare providers and community health advocates as an acceptable, feasible \nand cost-effective two-way communication tool, although concerns were raised \nabout its hidden costs, privacy and security issues. Acceptability of WhatsApp \nbased information dissemination amongst the study participants was high with \nreported benefits of increased awareness of eye diseases, their preventative \nmanagement, remedial measures and the availability of affordable eyecare \nservices. Additionally, study participants found WhatsApp technology appealing \nand intuitive. The resultant increase in self-confidence, consequent to heightened \nawareness, boosted social empowerment and enabled study participants to \nchallenge prevalent social and cultural norms. \nIn conclusion, this study demonstrated that WhatsApp can be effectively used as a \nsuitable vehicle of information dissemination on eye care in mediating a behavioural \nchange in deprived settings. Findings from this study may be considered in \ndeveloping policies that develop and disseminate eye care information. The wider \nimplications and impact of this study lies in disseminating healthcare information \nrelated to other important public health issues to the marginal population.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".