An mHealth Intervention to Improve Guardians’ Adherence to Children’s Follow-Up Care for Acute Lymphoblastic Leukemia in Tanzania (GuardiansCan Project): Protocol for a Development and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Cancer is a leading cause of death during childhood and in low- and middle-income countries survival rates can be as low as 20%. A leading reason for low childhood cancer survival rates in low- and middle-income countries such as Tanzania is treatment abandonment. Contributing factors include poor communication between health care providers and children's guardians, insufficient cancer knowledge, and psychological distress. OBJECTIVE: Our aim is to respond to Tanzanian guardians' poor adherence to children's follow-up care after treatment for acute lymphoblastic leukemia with the help of mobile health (mHealth) technology. Our goal is to increase guardians' adherence to children's medications and follow-up visits and to decrease their psychological distress. METHODS: Following the Medical Research Council framework for developing and evaluating complex interventions, we will undertake the GuardiansCan project in an iterative phased approach to develop an mHealth intervention for subsequent testing. Public contribution activities will be implemented throughout via the establishment of a Guardians Advisory Board consisting of guardians of children with acute lymphoblastic leukemia. We will examine the acceptability, feasibility, and perceived impact of Guardians Advisory Board activities via an impact log and semistructured interviews (study I). In phase 1 (intervention development) we will explore guardians' needs and preferences for the provision of follow-up care reminders, information, and emotional support using focus group discussions and photovoice (study II). We will then co-design the mHealth intervention with guardians, health care professionals, and technology experts using participatory action research (study III). In phase 2 (feasibility), we will examine clinical, methodological, and procedural uncertainties associated with the intervention and study procedures to prepare for the design and conduct of a future definitive randomized controlled trial using a single-arm pre-post mixed methods feasibility study (study IV). RESULTS: Data collection for the GuardiansCan project is anticipated to take 3 years. We plan to commence study I by recruiting Guardians Advisory Board members in the autumn of 2023. CONCLUSIONS: By systematically following the intervention development and feasibility phases of the Medical Research Council Framework, and working alongside an advisory board of guardians, we intend to develop an acceptable, culturally appropriate, feasible, and relevant mHealth intervention with the potential to increase guardians' adherence to children's follow-up care after treatment of acute lymphoblastic leukemia, leading to a positive impact on children's health and chances to survive, and reducing distress for guardians. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48799.
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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 | 0.028 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.005 |
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".