Eliciting women’s cervical screening preferences: a mixed methods systematic review protocol
Bibliographic record
Abstract
BACKGROUND: With the accumulation of evidence regarding potential harms of cancer screening in recent years, researchers, policy-makers, and the public are becoming more critical of population-based cancer screening. Consequently, a high-quality cancer screening program should consider individuals' values and preferences when determining recommendations. In cervical cancer screening, offering women autonomy is considered a "person-centered" approach to health care services; however, it may impact the effectiveness of the program should women choose to not participate. As part of a larger project to investigate women's cervical screening preferences and correlates of these preferences, this systematic review will capture quantitative and qualitative investigations of women's cervical screening preferences and the methods used to elicit them. DESIGN AND METHODS: This mixed methods synthesis will use a thematic analysis approach to synthesize qualitative, quantitative, and mixed methods evidence. This protocol describes the methods that will be used in this investigation. A search strategy has been developed with a health librarian and peer reviewed using PRESS. Based on this strategy, five databases and the gray literature will be searched for studies that meet the inclusion criteria. The quality of the included individual studies will be examined using the Mixed Methods Appraisal Tool. Three reviewers will extract data from the primary studies on the tools or instruments used to elicit women's preferences regarding cervical cancer screening, theoretical frameworks used, outcomes measured, the outstanding themes from quantitative and qualitative evidence, and the identified preferences for cervical cancer screening. We will describe the relationships between study results and the study population, "intervention" (e.g., tool or instrument), and context. We will follow the PRISMA reporting guideline. We will compare findings across studies and between study methods (e.g., qualitative versus quantitative study designs). The strength of the synthesized findings will be assessed using the validated GRADE and CERQual tool. DISCUSSION: This review will inform the development of a tool to elicit women's cervical screening preferences. Understanding the methods used to elicit women's preferences and what is known about women's cervical screening preferences will be useful for guideline developers who wish to incorporate a woman-centered approach specifically for cervical screening guidelines. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016035737.
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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 | 0.182 | 0.151 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 0.013 |
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".