A protocol for evaluating a multi-level implementation theory to scale up obstetric triage in referral hospitals in Ghana
Bibliographic record
Abstract
Abstract Background Ghana significantly reduced maternal and newborn mortality between 1990 and 2015, largely through efforts focused on improving access to care. Yet achieving further progress requires improving the quality and timeliness of care. Beginning in 2013, Ghana Health Service and Kybele, a US-based non-governmental organization, developed an innovative obstetric triage system to help midwives assess, diagnosis, and determine appropriate care plans more quickly and accurately. In 2019, efforts began to scale this successful intervention into six additional hospitals. This protocol describes the theory-based implementation approach guiding scale-up and presents the proposed mixed-methods evaluation plan. Methods An implementation theory was developed to describe how complementary implementation strategies would be bundled into a multi-level implementation approach. Drawing on the Interactive Systems Framework and Evidenced Based System for Implementation Support, the proposed implementation approach is designed to help individual facilities develop implementation capacity and also build a learning network across facilities to support the implementation of evidence-based interventions. A convergent design mixed methods approach will be used to evaluate implementation with relevant data drawn from tailored assessments, routinely collected process and quality monitoring data, textual analysis of relevant documents and WhatsApp group messages, and key informant interviews. Implementation outcomes of interest are acceptability, adoption, and sustainability. Discussion The past decade has seen a rapid growth in the development of frameworks, models, and theories of implementation, yet there remains little guidance on how to use these to operationalize implementation practice. This study proposes one method for using implementation theory, paired with other kinds of mid-level and program theory, to guide the replication and evaluation of a clinical intervention in a complex, real-world setting. The results of this study should help to provide evidence of how implementation theory can be used to help close the “know-do” gap.
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 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 | 0.200 | 0.158 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.079 | 0.012 |
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".