Psychological Factors Influencing Pain Perception and Experience in Women Undergoing Mammography: Protocol for a Systematic Review
Bibliographic record
Abstract
Background: Breast cancer is the most frequently diagnosed cancer among women worldwide and a leading cause of cancer-related mortality. Mammographic screening significantly improves the early detection and survival rates. However, the pain and discomfort experienced during mammography, primarily due to breast compression, can serve as major deterrents to participation in routine screening programs. Psychological factors such as anxiety, fear, and pain catastrophizing have been shown to influence pain perception and experience during mammography. These factors may affect women's decisions to participate in or avoid screening, undermining public health efforts for early detection. Objective: This study aims to synthesize the scientific literature on the psychological factors influencing pain perception and experience in women undergoing mammography. Methods: This systematic review protocol is in accordance with the 2015 PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines. Eligible studies will include randomized controlled trials and observational designs that examine psychological factors-such as anxiety, catastrophizing, and related constructs-in relation to pain perception and experience among women undergoing screening or diagnostic mammography. The primary outcome is women's perception and experience of pain during mammography, and the role of psychological factors may influence it, while secondary outcomes include pain intensity and pain-related distress, measured with validated pain scales or self-reported questionnaires. There will be no restriction on publication year, but only peer-reviewed, full-text articles in English will be included. Gray literature will be excluded. A systematic search will be conducted in PubMed, Scopus, and PsycINFO using database-specific strategies with keywords and Boolean operators; reference lists of included studies will also be screened. Study selection and data extraction will be performed independently by two reviewers. Risk of bias will be assessed using the Joanna Briggs Institute critical appraisal tools. Data will be synthesized narratively, with thematic grouping of psychological factors and tabulation of study characteristics. Due to anticipated heterogeneity across populations, study designs, and outcome measures, a meta-analysis will not be feasible; instead, greater interpretive weight will be given to findings from studies judged to have a lower risk of bias. Results: The database search has been completed in September 2025. Data extraction and organization into summary tables are scheduled to be finished by December 2025, followed by a narrative synthesis of findings. The systematic review manuscript is planned for submission to a peer-reviewed journal in January 2026. Conclusions: This protocol outlines the first systematic review to comprehensively investigate women's perception and experience of pain during mammography and the psychological factors, such as anxiety, depression, fear, and coping strategies, that may influence it. The review aims to generate evidence-based insights that will inform clinical practice and guide the development of targeted interventions designed to reduce discomfort, improve screening experiences, and increase participation in breast cancer screening programs. The authors also plan to disseminate the findings through publication in a peer-reviewed journal.
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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 | 0.051 | 0.070 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.006 |
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".