Exploring gestational age, and birth weight assessment in Thatta district, Sindh, Pakistan: Healthcare providers’ knowledge, practices, perceived barriers, and the potential of a mobile app for identifying preterm and low birth weight
Bibliographic record
Abstract
INTRODUCTION: Reliable methods for identifying prematurity and low birth weight (LBW) are crucial to ending preventable deaths in newborns. This study explored healthcare providers' (HCPs) knowledge, practice, perceived barriers in assessing gestational age and birth weight and their referral methods for preterm and LBW infants. The study additionally assessed the potential of using a mobile app for the identification and referral decision of preterm and LBW. METHODS: This qualitative descriptive study was conducted in Thatta District, Sindh, Pakistan. Participants, including doctors, nurses, lady health visitors, and midwives, were purposefully selected from a district headquarter hospital, and private providers in the catchment area of Global Network's Maternal and Newborn Health Registry (MNHR). Interviews were conducted using an interview guide after obtaining written informed consent. Audio recordings of the interviews were transcribed and analyzed using NVIVO® software with an inductive approach. RESULTS: The HCPs had extensive knowledge about antenatal and postnatal methods for assessing gestational age. They expressed a preference for antenatal ultrasound due to the perceived accuracy, though accept practical barriers including workload, machine malfunctions, and cost. Postnatal assessment using the Ballard score was only undertaken sparingly due to insufficient training and subjectivity. All HCPs preferred electronic weighing scales for birth weight Barriers encountered included weighing scale calibration and battery issues. There was variation in the definition of prematurity and LBW, leading to delays in referral. Limited resources, inadequate education, and negative parent past experiences were barriers to referral. Foot length measurements were not currently being used. While mobile apps are felt to have potential, unreliable electricity supply and internet connectivity are barriers. CONCLUSION: The HCPs in this study were knowledgeable in terms of potential tools, but acknowledged the logistical and parental barriers to implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".