Scaling up the '24/7 BHU' strategy to provide round-the-clock maternity care in Punjab, Pakistan: A theory-driven, co-produced implementation study
Bibliographic record
Abstract
Abstract BackgroundPakistan’s maternal mortality rate remains persistently high at 186/100,000 live births. The country’s government-run first-level health care facilities, the Basic Health Units (BHU), are an important source of maternity care for rural women. However, BHUs only operate on working days from 8.00 am to 2.00 pm. Recognizing this severely constrains access to maternity services, the government is implementing the ‘24/7 BHU Initiative’ to upgrade BHUs to provide round-the-clock care. Although based on a successful pilot project, initial reports reveal challenges in scaling up the initiative. This implementation research project aims to address a key concern of the Government of Punjab: How can the 24/7 Basic Health Unit (BHU) initiative be successfully implemented at scale to provide high quality, round-the-clock skilled maternity care in rural Punjab? MethodsThe project consists of two overlapping work packages (WP). WP1 includes three modules generating data at directorate, district and BHU levels. Module 1 uses document analysis and policy-maker interviews to explicate programme theory and begin to build a system model. Module 2 compares government-collected data with data generated from a survey of 1500 births to assess BHU performance. Module 3 uses institutional ethnographies in 4-5 BHUs in three districts to provide a detailed system understanding and identify processes that influence scale-up. WP2 includes two modules. First, two workshops and regular meetings with stakeholders integrate WP1 findings, identify feasible changes and establish priorities. Next, "change ideas" are selected for testing in one district and 2-3 BHUs through carefully documented pilots using the PDSA improvement approach. An integrated knowledge translation approach will engage key policy and practice stakeholders throughout the project. DiscussionThis theory driven implementation research project will co-produce significant new understandings of the wider system in which the ‘24/7 BHU’ initiative is being implemented, and actionable knowledge that will highlight ways the implementation processes might be modified to enable BHUs to meet service provision goals. This study will also produce insights that will be relevant for other South Asian and LMICs that experience similar challenges of programme scale-up and delivery of maternal health services to remote and marginalised communities.
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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 | 0.033 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".