Barriers and facilitators to the implementation of cell phone interventions to improve the use of family planning services among women in Sub-Saharan Africa: a systematic review
Bibliographic record
Abstract
Abstract Background Mobile health (mHealth) interventions are being tested to improve contraceptive uptake in SubSaharan Africa (SSA). However, few attempts have systematically reviewed the mHealth programs aiming to improve family planning (FP) services among women in SSA. This review identifies and highlights facilitators and barriers to implementing cell phone interventions designed to target women FP services. Methods Databases including PubMed, CINAHL, Epistemonikos, Embase, and Global Health were systematically searched for studies from January 01, 2010, to December 31, 2020, to identify various mHealth interventions used to improve the use of FP services among women in SSA. Two authors independently selected eligible publications based on inclusion/exclusion criteria, assessed study quality and extracted data using a pre-defined data extraction sheet. In addition, a content analysis was conducted using a validated extraction grid with a pre-established categorization of barriers and facilitators. Results The search strategy led to a total of 8,188 potentially relevant papers, of which 16 met the inclusion criteria. The majority of included studies evaluated the impact of mHealth interventions on FP services; access (n = 9) and use of FP outcomes (n = 6). The most-reported cell phone use was for women reproductive health education, contraceptive knowledge and use. Barriers and facilitators of the use of mhealth were categorized into three main outcomes: behavioral outcomes, data collection and reporting, and health outcomes. mHealth interventions addressed barriers related to provider prejudice, stigmatization, discrimination, lack of privacy, and confidentiality. The studies also identified barriers to uptake of mHealth interventions for FP services, including decreased technological literacy and lower linguistic competency. Conclusion The review provides detailed information about the implementation of mobile phones at different healthcare system levels to improve FP services; outcomes. Barriers to uptake mHealth interventions must be adequately addressed to increase the potential use of mobile phones to improve access to sexual reproductive health awareness and family planning services. Systematic review registration PROSPERO CRD42020220669 (December 14, 2020)
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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 | 0.020 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".