Training of healthcare providers and use of long‐acting reversible contraception in low‐ and middle‐income countries: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Unintended pregnancy, a major global health issue resulting in unsafe terminations of pregnancy and maternal deaths in low- and middle-income countries, could be significantly reduced through increased use of modern contraception, including long-acting reversible contraceptives (LARC). Training of healthcare providers to administer such contraceptives may improve uptake. We conducted a systematic review to collate the end-user uptake data following training of healthcare providers in low- and middle-income countries. MATERIAL AND METHODS: We searched PubMed, Embase, the Global Health Library and the Cochrane Library up to 23 May 2020. The review was restricted to low- and middle-income countries and focused on healthcare providers who had received training in LARC. Studies that reported contraceptive uptake among women, preference of LARC among healthcare workers and/or women, and unplanned pregnancies within 12 months of LARC initiation were included. All included studies underwent quality assessment using either the Cochrane Risk of Bias Tool or the Newcastle-Ottawa Scale. PROSPERO registration number CRD42020185291. RESULTS: A total of 28 studies (end-users n = 6 112 544) were included (27 cohort studies and one randomized trial). Nineteen studies were set in Africa, five in Asia, one in Central America and four were multi-country studies. Twenty-eight studies reported LARC use among women, and 25 studies found an increase in uptake of LARC by women using short-acting methods switching to longer-acting methods or by recruiting new users of LARC. The randomized controlled trial was assessed as high quality and reported positive findings; however, there was great heterogeneity in the type of intervention and of how outcomes were measured among the other included studies. Further, the quality of these studies varied, although it should be noted that the poor-quality studies reflected the trends of those of higher quality. CONCLUSIONS: Despite heterogeneity, current evidence indicates that training of healthcare providers in LARC may increase the uptake among women in low- and middle-income countries. More robust studies are warranted to inform policy.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".