Barriers and Facilitators to Shared Decision-Making for HPV Self-Sampling in LMICs: A Thematic Analysis
Bibliographic record
Abstract
Cervical cancer is the second most common cancer among women in lower-middle-income countries (LMICs), contributing to the disproportionately high number of disability-adjusted life years in these regions.1,2 Shared decision-making (SDM) is a healthcare model that promotes patient-centered care by involving patients and family caregivers in collaborative treatment decision-making.3 Despite relatively high SDM awareness in high-income countries (HICs), seven studies have highlighted low awareness among healthcare professionals in LMICs, with cultural and operational barriers impeding its practice.4 The HPV-Automated Visual Evaluation (PAVE) Study is a multinational initiative focused on improving cervical cancer prevention in resource-constrained regions.5 This study aims to investigate stakeholders' understanding, views, and attitudes towards cervical cancer prevention and screening, and their preferences for receiving information and participating in decision-making.5 This will be achieved by analyzing the qualitative interviews with scientific experts and healthcare providers from the four participating sites (Brazil, El Salvador, Nigeria, and Tanzania).5 Thematic analysis is being applied to the interview transcripts to identify common themes and patterns, and enhance our understanding of stakeholder perspectives. Due to structural and cultural differences between HICs and LMICs, we expect that tailoring SDM to address specific barriers in LMICs will enhance its acceptance and utilization, leading to improved patient outcomes and satisfaction in cervical cancer prevention and treatment. If validated, the findings from the PAVE study may inform the widespread adoption and enhancement of cervical cancer prevention programs on a global scale. References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [published correction appears in CA Cancer J Clin. 2020 Jul;70(4):313. doi: 10.3322/caac.21609]. CA Cancer J Clin. 2018;68(6):394-424. doi:10.3322/caac.21492 Momenimovahed, Z., Mazidimoradi, A., Maroofi, P., Allahqoli, L., Salehiniya, H., & Alkatout, I. (2023). Global, regional and national burden, incidence, and mortality of cervical cancer. Cancer reports (Hoboken, N.J.), 6(3), e1756. https://doi.org/10.1002/cnr2.1756 Faiman B, Tariman JD. Shared Decision Making: Improving Patient Outcomes by Understanding the Benefits of and Barriers to Effective Communication. Clin J Oncol Nurs. 2019;23(5):540-542. doi:10.1188/19.CJON.540-542 Sam S, Sharma R, Corp N, Igwesi-Chidobe C, Babatunde OO. Shared decision making in musculoskeletal pain consultations in low- and middle-income countries: a systematic review. Int Health. 2020;12(5):455-471. doi:10.1093/inthealth/ihz077 de Sanjosé S, Perkins RB, Campos NG, et al. Design of the HPV-Automated Visual Evaluation (PAVE) Study: Validating a Novel Cervical Screening Strategy. Preprint. medRxiv. 2023;2023.08.30.23294826. Published 2023 Oct 23. doi:10.1101/2023.08.30.23294826
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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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".