Assessing the Usability of a Task-Shifting Device for Inserting Subcutaneous Contraceptive Implants for Use in Low-Income Countries
Bibliographic record
Abstract
Abstract Women in low- and middle-income countries (LMICs) have limited access to long-acting contraceptives. Access to long-acting contraceptives, such as subcutaneous contraceptive implants, could be increased by task-shifting implant administration from advanced to minimally trained healthcare providers. The objective of this study was to investigate the usability of a task-shifting device for administering subcutaneous contraceptive implants. Healthcare providers (n = 128) from multiple health centers in Ethiopia were trained to administer implants on an arm simulator with the traditional method and a method using the device. Participants were observed while inserting implants into the arm simulator, and procedural error rates were calculated. Observations were analyzed using an iterative inductive coding methodology. For the device-assisted method, minimally trained healthcare providers had larger procedural error rates than other professions (p = 0.002). For the traditional method, physicians had larger procedural error rates than nurses and midwives (p = 0.03). Several procedural errors were identified such as participants inserting and removing the trocar and plunger completely or inserting and/or removing the trocar too far or not enough. These findings reinforce the importance of performing formative usability testing during the early phases of a medical device design process, considering users' mental models, and avoiding assumptions about healthcare providers' abilities.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".