Scale-up of a novel vital signs alert device to improve maternity care in Sierra Leone: a mixed methods evaluation of adoption
Bibliographic record
Abstract
Abstract Background The CRADLE (Community blood pressure monitoring in Rural Africa: Detection of underLying pre-Eclampsia) Vital Signs Alert device—designed specifically to improve maternity care in low resource settings—had varying impact when trialled in different countries. To better understand the contextual factors that may contribute to this variation, this study retrospectively evaluated the adoption of CRADLE, during scale-up in Sierra Leone. Methods This was a mixed methods study. A quantitative indicator of adoption (the proportion of facilities trained per district) was calculated from existing training records, then focus groups were held with ‘CRADLE Champions’ in each district (n = 32), to explore adoption qualitatively. Template Analysis was used to deductively interpret qualitative data, guided by the NASSS (non-adoption, abandonment, scale-up, spread, sustainability) Framework. Findings Substantial but non-significant variation was found in the proportion of facilities trained in each district (range 59–90%) [X2 (7, N = 8) = 10.419, p = 0.166]. Qualitative data identified complexity in two NASSS domains that may have contributed to this variation: ‘the technology’ (for example, charging issues, difficulty interpreting device output and concerns about ongoing procurement) and ‘the organisation’ (for example, logistical barriers to implementing training, infighting and high staff turnover). Key strategies mentioned to mitigate against these issues included: transparent communication at all levels; encouraging localised adaptations during implementation (including the involvement of community leaders); and selecting Champions with strong soft skills (particularly conflict resolution and problem solving). Conclusions Complexity related to the technology and the organisational context were found to influence the adoption of CRADLE in Sierra Leone, with substantial inter-district variation. These findings emphasise the importance of gaining an in-depth understanding of the specific system and context in which a new healthcare technology is being implemented. This study has implications for the ongoing scale-up of CRADLE, and for those implementing or evaluating other health technologies in similar contexts.
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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 | 0.086 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".