A qualitative study to explore the experience of parents of newborns admitted to neonatal care unit in rural Rwanda
Bibliographic record
Abstract
BACKGROUND: Neonatal Care Units (NCUs) provide special care to sick and small newborns and help reduce neonatal mortality. For parents, having a hospitalized newborn can be a traumatic experience. In sub-Saharan Africa, there is limited literature about the parents' experience in NCUs. OBJECTIVE: Our study aimed to explore the experience of parents in the NCU of a rural district hospital in Rwanda. METHODS: A qualitative study was conducted with parents whose newborns were hospitalized in the Ruli District Hospital NCU from September 2018 to January 2019. Interviews were conducted using a semi-structured guide in the participants' homes by trained data collectors. Data were transcribed, translated, and then coded using a structured code book. All data were organized using Dedoose software for analysis. RESULTS: Twenty-one interviews were conducted primarily with mothers (90.5%, n = 19) among newborns who were most often discharged home alive (90.5%, n = 19). Four themes emerged from the interviews. These were the parental adaptation to having a sick neonate in NCU, adaptation to the NCU environment, interaction with people (healthcare providers and fellow parents) in the NCU, and financial stressors. CONCLUSION: The admission of a newborn to the NCU is a source of stress for parents and caregivers in rural Rwanda, however, there were several positive aspects which helped mothers adapt to the NCU. The experience in the NCU can be improved when healthcare providers communicate and explain the newborn's status to the parents and actively involve them in the care of their newborn. Expanding the NCU access for families, encouraging peer support, and ensuring financial accessibility for neonatal care services could contribute to improved experiences for parents and families in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".