Another Best Practice: Leveraging User and Stakeholder Perspectives to Improve and Refine Existing Medical Products
Bibliographic record
Abstract
See related articles by Burke et al. and Callahan et al. With more than 74 million users, injectable contraceptives are one of the most widely used methods of contraception globally and remain the most prevalent method in sub-Saharan Africa.1 Depo medroxyprogesterone acetate (DMPA) is the most commonly used injectable contraceptive and is available in both a 3-month intramuscular formulation (DMPA-IM, 150 mg/ml) administered by providers and a 3-month subcutaneous formulation (DMPA-SC, 104 mg/0.65 ml), which is preloaded into a Uniject and can be either administered by providers or self-administered. Although both DMPA formulations are widely used, discontinuation is common; clients frequently cite concerns about side effects (e.g., contraceptive-induced menstrual changes) and delays in expected return to fertility.2,3 Adequate counseling can ease such concerns.4 However, for clients experiencing negative side effects, counseling cannot alleviate the symptoms. These clients have the unenviable choice of either continuing despite distressing side effects, switching to another method that is possibly less effective, or discontinuing and facing the risk of unintended pregnancy. To meet the family planning needs of these individuals and others who do not want to become pregnant but have concerns about hormonal method use, it is critical to facilitate access to a wider range of methods, including through the development of new nonhormonal contraceptive options. Recognizing that research and development of promising new products will take time, it is also important to refine existing products to increase accessibility, affordability, acceptability, and satisfaction. Although studies that yield incremental improvements to existing contraceptive methods will not address every method-related concern, this research may reduce some of the barriers users face to access and continue using their chosen method. This work can include studies to:
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".