Acceptability of an mHealth Family Self-management Intervention (myFAMI) for Pediatric Transplantation Families: Qualitative Focus
Bibliographic record
Abstract
BACKGROUND: Around 1800 pediatric transplantations were performed in 2021, which is approximately 5% of the annual rate of solid organ transplantations carried out in the United States. Effective family self-management in the transition from hospital to home-based recovery promotes successful outcomes of transplantation. The use of mHealth to deliver self-management interventions is a strategy that can be used to support family self-management for transplantation recipients and their families. OBJECTIVE: The study aims to evaluate the acceptability of an mHealth intervention (myFAMI) that combined use of a smartphone app with triggered nurse communication with family members of pediatric transplantation recipients. METHODS: This is a secondary analysis of qualitative data from family members who received the myFAMI intervention within a larger randomized controlled trial. Eligible participants used the app in the 30-day time frame after discharge and participated in a 30-day postdischarge telephone interview. Content analysis was used to generate themes. RESULTS: A total of 4 key themes were identified: (1) general acceptance, (2) positive interactions, (3) home management after hospital discharge, and (4) opportunities for improvement. CONCLUSIONS: Acceptability of the intervention was high. Family members rated the smartphone application as easy to use. myFAMI allowed the opportunity for families to feel connected to and engage with the medical team while in their home environment. Family members valued and appreciated ongoing support and education specifically in this first 30 days after their child's hospital discharge and many felt it contributed positively to the management of their child's medical needs at home. Family members provided recommendations for future refinement of the app and some suggested that a longer follow-up period would be beneficial. The development and refinement of mHealth care delivery strategies hold potential for improving outcomes for solid organ transplantation patients and their families and as a model to consider in other chronic illness populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT03533049; https://clinicaltrials.gov/ct2/show/NCT03533049.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".