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Record W4403757979 · doi:10.1101/2024.10.24.24316071

Impact of the training, support and access model (TSAM) on patient health outcomes in Rwanda: controlled longitudinal study

2024· preprint· en· W4403757979 on OpenAlexaff
Celestin Hategeka, Larry D. Lynd, Cynthia Kenyon, Anaclet Ngabonzima, Isaac Luginaah, David F. Cechetto, Michael R. Law

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsWestern UniversityLondon Health Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsTraining (meteorology)Longitudinal studyLongitudinal dataMedicinePsychologyDemographyGeographySociology

Abstract

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Abstract Achieving maternal and newborn health (MNH) related Sustainable Development Goal targets will require high-quality health systems in low– and middle-income countries. While over 90% of deliveries in Rwanda take place in health facilities, maternal and neonatal mortality rates remain high. In an effort to bolster quality of care provided to women and newborns to ultimately reduce morbidity and mortality, the Training, Support and Access Model (TSAM) clinical mentorship was established in 10 district hospitals in Rwanda in 2017. We evaluated the impact of the TSAM clinical mentorship intervention on maternal and newborn health outcomes. We used monthly time series data from the DHIS2-enabled Rwanda health management information system from February 2014 to February 2020 to assess the impact of the TSAM intervention on outcomes of care for MNH in intervention hospitals relative to concurrent control hospitals. Using a controlled quasi-experimental interrupted time series analysis, we estimated changes in rates of inpatient mortality and morbidity for MNH associated with the implementation of the TSAM clinical mentorship. The study cohort included 25 hospitals (10 TSAM hospitals and 15 control hospitals) that collectively reported 339,850 hospital deliveries and 94,584 neonatal hospital admissions. We found that the implementation of the TSAM clinical mentorship intervention was associated with a two-years reduction of 84% in the obstetrical complication case fatality rate, 32% in hospital neonatal mortality rate, 30% in postpartum hemorrhage incidence rate, and 48% in neonatal asphyxia incidence rate in TSAM hospitals relative to control hospitals. However, the stillbirth rate did not decline following the TSAM intervention. We found that a quality improvement strategy that employed continuous quality improvement approaches using onsite clinical mentorship of health providers along with involvement of health facility leadership to facilitate the improvement was associated with improvements in MNH in Rwanda. Our findings provide evidence that can justify the scale up of TSAM across the country and potentially in other similar settings. Summary box What is already known? Poor quality of healthcare is currently a bigger driver of excess maternal and neonatal mortality than under-utilization of health facilities in many low– and middle-income countries (LMICs). Achieving maternal and newborn health related Sustainable Development Goal targets will require high-quality health systems in LMICs. What does this study add? The Training, Support and Access Model (TSAM) clinical mentorship implemented in 10 Rwandan district hospitals to bolster quality of care provided to women and newborns was associated with a reduction in in-hospital maternal and newborn deaths. However, the (intrapartum) stillbirth rate did not decline following the TSAM intervention. The TSAM intervention was associated with a significant decline in in-hospital maternal and neonatal morbidity (e.g., incidence of postpartum hemorrhage and neonatal asphyxia). What do the new findings imply? Employing continuous quality improvement approaches using onsite clinical mentorship of health providers along with involvement of health facility leadership to facilitate the improvement can be an effective strategy to improve maternal and newborn health outcomes. Quasi-experimental methods leveraging routine health information systems data can be useful to study impact of health system improvement interventions in low-resource settings.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.225
GPT teacher head0.529
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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