Identifying opportunities to support patient-centred care for ductal carcinoma in situ: qualitative interviews with clinicians
Bibliographic record
Abstract
Abstract Background Women with ductal carcinoma in situ (DCIS) report poor patient-clinician communication, and long-lasting confusion and anxiety about their treatment and prognosis. Research shows that patient-centred care (PCC) improves patient experience and outcomes. Little is known about the clinician experience of delivering PCC for DCIS. This study characterized communication challenges faced by clinicians, and interventions they need to improve PCC for DCIS. Methods Purposive and snowball sampling were used to recruit Canadian clinicians by specialty, gender, years of experience, setting, and geographic location. Qualitative interviews were conducted by telephone. Data were analyzed using constant comparison. Findings were mapped to a cancer-specific, comprehensive PCC framework to identify opportunities for improvement. Results Clinicians described approaches they used to address the PCC domains of fostering a healing relationship, exchanging information, and addressing emotions, but do not appear to be addressing the domains of managing uncertainty, involving women in making decisions, or enabling self-management. However, many clinicians described challenges or variable practices for all PCC domains but fostering a healing relationship. Clinicians vary in describing DCIS as cancer based on personal beliefs. When exchanging information, most find it difficult to justify treatment while assuring women of a good prognosis, and feel frustrated when women remain confused despite their efforts to explain it. While they recognize confusion and anxiety among women, clinicians said that patient navigators, social workers, support groups and high-quality information specific to DCIS are lacking. Despite these challenges, clinicians said they did not need or want communication interventions. Conclusions Findings represent currently unmet opportunities by which to help clinicians enhance PCC for DCIS, and underscore the need for supplemental information and supportive care specific to DCIS. Future research is needed to develop and test communication interventions that improve PCC for DCIS. If effective and widely implemented, this may contribute to improved care experiences and outcomes for women diagnosed with and treated for DCIS.
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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 | 0.035 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".