Caregiver Acceptability of Mobile Phone Use for Pediatric Cancer Care in Tanzania: Cross-sectional Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: There is a 60% survival gap between children diagnosed with cancer in low- and middle-income countries (LMICs) and those in high-income countries. Low caregiver knowledge about childhood cancer and its treatment results in presentation delays and subsequent treatment abandonment in LMICs. However, in-person education to improve caregiver knowledge can be challenging due to health worker shortages and inadequate training. Due to the rapid expansion of mobile phone use worldwide, mobile health (mHealth) technologies offer an alternative to delivering in-person education. OBJECTIVE: The aim of this study is to assess patterns of mobile phone ownership and use among Tanzanian caregivers of children diagnosed with cancer as well as their acceptability of an mHealth intervention for cancer education, patient communication, and care coordination. METHODS: In July 2017, caregivers of children <18 years diagnosed with cancer and receiving treatment at Bugando Medical Centre (BMC) were surveyed to determine mobile phone ownership, use patterns, technology literacy, and acceptability of mobile phone use for cancer education, patient communication, and care coordination. Descriptive statistics were generated from the survey data by using mean and SD values for continuous variables and percentages for binary or categorical variables. RESULTS: All eligible caregivers consented to participate and completed the survey. Of the 40 caregivers who enrolled in the study, most used a mobile phone (n=34, 85%) and expressed high acceptability in using these devices to communicate with a health care provider regarding treatment support (n=39, 98%), receiving laboratory results (n=37, 93%), receiving reminders for upcoming appointments (n=38, 95%), and receiving educational information on cancer (n=35, 88%). Although only 9% (3/34) of mobile phone owners owned phones with smartphone capabilities, about 74% (25/34) self-reported they could view and read SMS text messages. CONCLUSIONS: To our knowledge, this is the first study to assess patterns of mobile phone ownership and use among caregivers of children with cancer in Tanzania. The high rate of mobile phone ownership and caregiver acceptability for a mobile phone-based education and communication strategy suggests that a mobile phone-based intervention, particularly one that utilizes SMS technology, could be feasible in this setting.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".