Decision-Making About Screening Mammography: Exploring Perceptions of Family Physicians and Patients
Bibliographic record
Abstract
Background: Mammography screening discussions are assumed by family physicians to be an integral part of the process of deciding whether to begin screening, with women attending their family medicine clinic to discuss screening and receiving a radiology requisition if agreed. For decisions such as screening, shared decision-making (SDM) can help promote informed choice. Little research has been conducted to understand SDM for screening mammography from the perspectives of both patients and physicians. Research Question: Among Albertan family physicians and women of recommended screening age, what does each group understand of the benefits and harms of mammograms and how does communicating this knowledge and various external factors affect screening decisions? Methods: Convenience sampling was used to recruit family physicians, and women between 50-59 years of age in their practices, from Calgary family medicine clinics. Semi-structured interviews were conducted. The transcribed interviews were analyzed with reflexive thematic analysis. Results: Interviews were conducted with nine family physicians and eleven women from their practices. Interviews identified varying perspectives on screening mammography. Physicians emphasized individualized risk assessment, resource availability, and technological support, viewing screening as a collaborative decision based on evidence and tailored guidance. In contrast, patients focused on perceived risk, the importance of screening, personal experiences, and time limitations, often seeing screening as a necessary health measure with only one “correct” choice to be made. Four intersecting themes are developed: Consultation Complexity, The Influence and Impact of Technology on Decisions, Navigating Perceptions of Screening: A Routine or Choice, and Enhancing Informed Decisions. These various factors shaped the perceptions and behaviours around screening. Discussion: Discussions on screening mammography are influenced by a variety of factors including external sources of information, competing organizational messages, and technical challenges such as time available. Women receive information outside of the clinic, adding complexity to patient-provider interactions. When having screening discussions, family physicians must navigate these dynamics, addressing diverse perceptions and providing tailored communication to support informed decision-making.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".