Challenges and recommendations to improve implementation of phototherapy among neonates in Malawian hospitals
Bibliographic record
Abstract
BACKGROUND: Severe neonatal jaundice can result in long term morbidities and mortality when left untreated. Phototherapy is the main-stay intervention for treating moderate jaundice and for prevention of the development of severe jaundice. However, in resource-limited health care settings, phototherapy has been inconsistently used. The objective of this study is to evaluate barriers and facilitators for phototherapy to treat neonatal jaundice at Malawian hospitals. METHODS: We conducted a convergent mixed-method study comprised of a facility assessment and qualitative interviews with healthcare workers and caregivers in southern Malawi. The facility assessment was conducted at three secondary-level hospitals in rural districts. In-depth interviews following a semi-structured topic guide were conducted at a district hospital and a tertiary-level hospital. Interviews were thematically analysed in NVivo 12 software (QSR International, Melbourne, Australia). RESULTS: The facility assessment found critical gaps in initiating and monitoring phototherapy in all facilities. Based on a total of 31 interviews, participants identified key challenges in diagnosing neonatal jaundice, counselling caregivers, and availability of infrastructure. Participants emphasized the need for transcutaneous bilirubinometers to guide treatment decisions. Caregivers were sometimes fearful of potential harmful effects of phototherapy, which required adequate explanation to mothers and family members in non-medical language. Task shifting and engaging peer support for caregivers with concerns about phototherapy was recommended. CONCLUSION: Implementation of a therapeutic intervention is limited if accurate diagnostic tests are unavailable. The scale up of therapeutic interventions, such as phototherapy for neonatal jaundice, requires careful holistic attention to infrastructural needs, supportive services such as laboratory integration as well as trained human resources.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".