Evaluation of the FAST-M maternal sepsis intervention in Pakistan: A qualitative exploratory study
Bibliographic record
Abstract
The World Health Organization and partners developed and evaluated a maternity-specific sepsis care bundle called 'FAST-M' for low-resource settings. However, this bundle has not yet been studied in Asia. Our study sought to evaluate the perceptions of healthcare providers about the implementation of the FAST-M intervention in Pakistan.The study was conducted at a public sector hospital in Hyderabad. We conducted three focus group discussions with healthcare providers including doctors, nurses, and healthcare administrators (n = 22) who implemented the FAST-M intervention. The Consolidated Framework for Implementation Research was used as a guiding framework for data collection and analysis. The data were analyzed using a thematic analysis approach and deductive methods.Five overarching themes emerged: (I) FAST-M intervention and its significance including HCPs believing in the advantages of using the intervention to improve clinical practices; (II) Influence of outer and inner settings including non-availability of resources in the facility for sepsis care; (III) HCPs perceptions about sustainability, which were positive (IV) Integration into the clinical setting including HCPs views on the existing gaps, for example, shortage of HCPs and communication gaps, and their recommendations to improve these; and (V) Outcomes of the intervention including improved clinical processes and outcomes using the FAST-M intervention. Significant improvement in patient monitoring and FAST-M bundle completion within an hour of diagnosis of sepsis was reported by the HCPs.The healthcare providers' views were positive about the intervention, its outcomes, and long-term sustainability. The qualitative data provided findings on the acceptability of the overall implementation processes to support subsequent scaling up of the intervention.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".