Caregivers’ and nurses’ perceptions of the Smart Discharges Program for children with sepsis in Uganda: A descriptive qualitative study
Bibliographic record
Abstract
Abstract Sepsis arises when the body’s response to infection results in organ dysfunction. Among children hospitalized with suspected sepsis in low-income country settings, mortality rates following discharge are similar to mortality rates in hospital. The Smart Discharges Program uses a mobile health (mHealth) platform to identify children at high risk of post-discharge mortality to receive enhanced post-discharge care. This study sought to explore the perceptions of the caregivers and nurses of children enrolled into the Smart Discharges Program. We conducted a descriptive qualitative study that used a phenomenological approach. We conducted in-person focus group discussions (FGDs) with 30 caregivers of pediatric patients enrolled in the Smart Discharges Program and individual, semi-structured interviews with eight Smart Discharges Program nurses. The study was carried out at four hospitals in Uganda in 2019. Following thematic analysis, three key themes pertaining to the Smart Discharges program were identified: (1) Facilitators and barriers to follow-up care after discharge; (2) Changed behavior following discharge; and (3) Increased involvement of male caregivers. Facilitators included telephone/text message reminders, positive nurse-patient relationship, and the complementary aspects of the program. Resource constraints and negative experiences during post-discharge care seeking were reported as the most prominent barriers to post-discharge follow up. When provided with relevant and well-timed information, caregivers reported increased knowledge about post-discharge care and improvements in their ability to care for their child. Enrolment in the Smart Discharges Program also increased male caregiver involvement, which was reported as improved engagement in care, increased provision of resources and improved communication within the family and with the healthcare system. The Smart Discharges approach is an impactful strategy to improve pediatric post-discharge care, and similar approaches should be considered to improve the hospital to home transition in similar low-income country settings.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".