{"id":"W4387865309","doi":"10.1080/26410397.2023.2266305","title":"Lessons learned from developing and implementing digital health tools for self-managed abortion and sexual and reproductive healthcare in Canada, the United States, and Venezuela","year":2023,"lang":"en","type":"article","venue":"Sexual and Reproductive Health Matters","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Reproductive health; Abortion; Health care; Sexual and reproductive health and rights; Medicine; Nursing; Political science; Economic growth; Reproductive rights; Environmental health; Pregnancy; Biology; Population; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01285131,0.0004944552,0.0003494822,0.001265676,0.005975046,0.00826272,0.002154595,0.002486963,0.003134889],"category_scores_gemma":[0.02205659,0.0003262827,0.000499575,0.001629711,0.004717126,0.004389362,0.003794528,0.005516163,0.0002036109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02316181,"about_ca_system_score_gemma":0.09493448,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5856662,"about_ca_topic_score_gemma":0.7808304,"domain_scores_codex":[0.9890534,0.006144581,0.0003858362,0.0003969822,0.00127569,0.002743528],"domain_scores_gemma":[0.9885622,0.005331463,0.0003346582,0.0003541315,0.002314844,0.003102649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002521077,0.001681258,0.03645973,0.002695438,0.00006098563,0.002501953,0.1618632,0.001226441,0.001050436,0.03408572,0.05367424,0.7044486],"study_design_scores_gemma":[0.0001915473,0.0007501771,0.07539515,0.0101221,0.0001311411,0.001001116,0.5064312,0.001288868,0.001614567,0.01125953,0.3916591,0.0001554054],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3015326,0.04056048,0.003342353,0.5727876,0.001876582,0.0008098861,0.0003678054,0.0001393893,0.07858334],"genre_scores_gemma":[0.9134169,0.03244532,0.01282244,0.02771435,0.000279641,0.0002894774,0.0002234383,0.00006683209,0.01274166],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4143338,"threshold_uncertainty_score":0.8335478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1141990712308698,"score_gpt":0.4188516090004286,"score_spread":0.3046525377695588,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}