There’s an App for it: A systematic review of mobile apps providing information about abortion using a revised MARS scale
Bibliographic record
Abstract
Mobile applications (apps) are increasingly being used to access health-related information, but it may be challenging for consumers to identify accurate and reliable platforms. We conducted a systematic review of applications that provide information about abortion. We searched the iTunes and Google Play stores and queried professional networks to identify relevant apps. To evaluate the apps, we used the validated Mobile App Rating Scale (MARS) and added relevant abortion-specific elements. Two reviewers independently rated each app, and we report mean scores on a 5-point scale across the domains of engagement, functionality, esthetics, and information. We also rated app characteristics (including target population and reach), and number of desirable abortion-specific features. We defined recommended apps as those that achieved a score of 4.0 or above for the question: "would you recommend this app to people who may benefit from it?" Our search initially yielded 282 apps and we identified two additional apps through professional mailing lists. Most were irrelevant or not abortion-specific. We excluded 37 apps that sought to discourage users from seeking abortion. Only 10 apps met inclusion criteria for this review. The Euki app had the highest overall score (4.0). Half of the apps achieved a score of 3.0 or greater. Most of the apps had few desirable design features. Some apps provided significant information but had poor functionality. Only four apps met criteria for being recommended: Euki, Safe Abortion by Hesperian, Ipas Mexico, and Marie Stopes Mexico. In conclusion, we found few apps that provide unbiased information about abortion, and their quality varied greatly. App developers and abortion experts should consider designing additional apps that are clinically accurate, unbiased and well-functioning. We registered this review in the PROSPERO database (Registration # CRD42020195802).
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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 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".