The Impact of WhatsApp as a Health Education Tool in Albinism: Interventional Study
Bibliographic record
Abstract
BACKGROUND: Oculocutaneous albinism is a congenital disorder that causes hypopigmentation of the skin, hair, and eyes due to a lack of melanin. People with albinism are at increased risk of developing skin complications, such as solar keratosis and skin cancers, leading to higher morbidity. As education is crucial in managing albinism, leveraging information technology, such as WhatsApp, can provide an effective intervention for digital health education. OBJECTIVE: This study aims to assess the impact of WhatsApp as a tool for providing health education among people with albinism. METHODS: The design of the study was interventional. The intervention consisted of weekly health education sessions conducted in a WhatsApp group for the duration of 4 weeks. The topics discussed were knowledge of albinism, sun protection practices, the use of sunscreen, and myths about albinism. They were all covered in 4 WhatsApp sessions held in 4 separate days. A web-based questionnaire was filled out before and after the intervention by the participants. Mann-Whitney U test was used to compare the pre- and postknowledge scores. Spearman correlation was used to correlate data. RESULTS: The mean age of the study participants was 28.28 (SD 11.57) years. The number of participants was 140 in the preintervention period and 66 in the postintervention period. A statistically significant increase in overall knowledge (P=.01), knowledge of sunscreen (P=.01), and knowledge of sun protection (P<.01) was observed following the intervention. Before the intervention, a positive correlation was observed between age (r=0.17; P=.03) and education level (r=0.19; P=.02) with participants' overall knowledge. However, after the intervention, there was no significant correlation between knowledge and age or education level. A percentage increase of 5.23% was observed in the overall knowledge scores following the intervention. CONCLUSIONS: WhatsApp is an effective tool for educating people with albinism and can act as an alternative to the conventional methods of health education. It shows promising outcomes irrespective of the health literacy level of people with albinism. This educational intervention can positively impact behavior change and translate to consistent sun protection practices. The limitations of this study include the possibility of social desirability bias and data security.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".