Cultural Adaptation of Digital Knowledge Translation Tools for Acute Otitis Media in Low- to Middle-Income Countries: Mixed Methods Usability Study (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Acute otitis media (AOM) is the most common pediatric bacterial ear infection. AOM presents challenges to parents who lack accurate information. Digital knowledge translation tools offer a promising approach to communicating complex health information. We developed AOM knowledge translation tools for Canadian parents and augmented them for Pakistani parent end users. </sec> <sec> <title>OBJECTIVE</title> This pilot study aimed to (1) develop AOM knowledge translation tools for Canadian parents, (2) adapt the knowledge translation tools across cultural contexts, and (3) evaluate the usability of the adapted knowledge translation tools. </sec> <sec> <title>METHODS</title> Parents’ perceptions of the translated knowledge translation tools’ usability were explored using a mixed-methods design. We recruited parent participants from a hospital in Pakistan to complete usability surveys (n=47) and focus group interviews (n=21). Descriptive statistics and content analysis were used to analyze data. </sec> <sec> <title>RESULTS</title> Usability results showed the usefulness and effectiveness of both adapted knowledge translation tools. Parents reported preferring a digital media narrative format in their own language. Findings revealed that culturally adapted knowledge translation tools are effective in transferring health information to parents. </sec> <sec> <title>CONCLUSIONS</title> Digital knowledge translation tools offer a promising approach to improving health literacy and communicating complex health information to parents of children with AOM. Culturally adapting the tools generated important knowledge that will contribute to knowledge translation advancements. Evaluation of the tool effectiveness is a critical next step to exploring the impact of knowledge translation tools on child health outcomes. </sec>
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".