Facilitators and constraints to family integrated care in low‐resource settings informed the adaptation in Uganda
Bibliographic record
Abstract
AIM: Family Integrated Care (FICare) was developed in high-income countries and has not been tested in resource-poor settings. We aimed to identify the facilitators and constraints that informed the adaptation of FICare to a neonatal hospital unit in Uganda. METHODS: Maternal focus groups and healthcare provider interviews were conducted at Uganda's Jinja Regional Referral Hospital in 2020. Transcripts were analysed using inductive content analysis. An adaptation team developed Uganda FICare based on the identified facilitators and constraints. RESULTS: Participants included 10 mothers (median age 28 years) and eight healthcare providers (seven female, median age 41 years). Reducing healthcare provider workload, improving neonatal outcomes and empowering mothers were identified as facilitators. Maternal stress, maternal difficulties in learning new skills and mistrust of mothers by healthcare providers were cited as constraints. Uganda FICare focused on task-shifting important but neglected patient care tasks from healthcare providers to mothers. Healthcare providers learned how to respond to maternal concerns. Intervention material was adapted to prioritise images over text. Mothers familiar with FICare provided peer-to-peer support to other mothers. CONCLUSION: Uganda FICare shares the core values of FICare but was adapted to be feasible in low-resource 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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| 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".