Task Shifting Provision of Contraceptive Implants to Community Health Extension Workers: Results of Operations Research in Northern Nigeria
Bibliographic record
Abstract
BACKGROUND: Contraceptive use remains low in Nigeria, with only 11% of women reporting use of any modern method. Access to long-acting reversible contraceptives (LARCs) is constrained by a severe shortage of human resources. To assess feasibility of task shifting provision of implants, we trained community health extension workers (CHEWs) to insert and remove contraceptive implants in rural communities of Bauchi and Sokoto states in northern Nigeria. METHODS: We conducted 2- to 3-week training sessions for 166 selected CHEWs from 82 facilities in Sokoto state (September 2013) and 84 health facilities in Bauchi state (December 2013). To assess feasibility of the task shifting approach, we conducted operations research using a pretest-posttest design using multiple sources of information, including surveys with 151 trained CHEWs (9% were lost to follow-up) and with 150 family planning clients; facility observations using supply checklists (N = 149); direct observation of counseling provided by CHEWs (N = 144) and of their clinical (N = 113) skills; as well as a review of service statistics (N = 151 health facilities). The endline assessment was conducted 6 months after the training in each state. RESULTS: CHEWs inserted a total of 3,588 implants in 151 health facilities over a period of 6 months, generating 10,088 couple-years of protection (CYP). After practicing on anatomic arm models, most CHEWs achieved competency in implant insertions after insertions with 4-5 actual clients. Clinical observations revealed that CHEWs performed implant insertion tasks correctly 90% of the time or more for nearly all checklist items. The amount of information that CHEWs provided clients increased between baseline and endline, and over 95% of surveyed clients reported being satisfied with CHEWs' services in both surveys. The study found that supervisors not only observed and corrected insertion skills, as needed, during supervisory visits but also encouraged CHEWs to conduct more community mobilization to generate client demand, thereby promoting access to quality services. CHEWs identified a lack of demand in the communities as the major barrier for providing services. CONCLUSION: With adequate training and supportive supervision, CHEWs in northern Nigeria can provide high-quality implant insertion services. If more CHEWs are trained to provide implants and greater community outreach is conducted to generate demand, uptake of LARCs in Nigeria may increase.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".