Benefits and Challenges of Implementing an Adaptation of Family Integrated Care in a Ugandan Setting
Bibliographic record
Abstract
BACKGROUND: Family Integrated Care (FICare) integrates parents as partners in neonatal intensive care unit care. Our team adapted and implemented this approach in a Ugandan unit for hospitalized neonates. PURPOSE: This qualitative descriptive study examined the perceptions of mothers and healthcare professionals (HCPs) of the benefits and challenges of this new approach to care. METHODS: Fifty-one mothers of hospitalized neonates born weighing greater than 2000 g participated in the program. They were taught to assess neonate danger signs, feeding, and weight. After discharge, a subsample (n = 15) participated in focus groups to explore benefits and challenges of their participation in care. Interviews with 8 HCPs were also conducted for the same purpose. Transcripts from focus groups and interviews were analyzed using inductive content analysis to describe the benefits and challenges from the perspectives of mothers and HCPs. RESULTS: For mothers a benefit was decreased stress. Both mothers and HCPs reported that the knowledge and skills mothers acquired were a benefit as was their ability to apply these to the care of their neonate. Improved relations between mothers and HCPs were described, characterized by greater exchange of information and HCPs' attentiveness to mothers' assessments. Mothers felt ready for discharge and used their knowledge at home. HCPs noted a decrease in their workload. Challenges included the need for mothers to overcome fears about performing the tasks, their own well-being and literacy skills, and access to equipment. IMPLICATIONS FOR PRACTICE: Mothers' participation in their neonates' care can have benefits for them and their neonate.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".