Recommendations to improve patient‐centred care for ductal carcinoma in situ: Qualitative focus groups with women
Bibliographic record
Abstract
BACKGROUND: Patient-centred care (PCC) improves health-care experiences and outcomes. Women with ductal carcinoma in situ (DCIS) and clinicians have reported communication difficulties. Little prior research has studied how to improve communication and PCC for DCIS. OBJECTIVE: This study explored how to achieve PCC for DCIS. DESIGN: Canadian women treated for DCIS from five provinces participated in semi-structured focus groups based on a 6-domain cancer-specific PCC framework to discuss communication about DCIS. Data were analysed using constant comparative technique. SETTING AND PARTICIPANTS: Thirty-five women aged 30 to 86 participated in five focus groups at five hospitals. RESULTS: Women said their clinicians used multiple approaches for fostering a healing relationship; however, most described an absence of desired information or behaviour to exchange information, respond to emotions, manage uncertainty, make decisions and enable self-management. Most women were confused by terminology, offered little information about the risks of progression/recurrence, uninformed about treatment benefits and risks, frustrated with lack of engagement in decision making, given little information about follow-up plans or self-care advice, and received no acknowledgement or offer of emotional support. DISCUSSION AND CONCLUSIONS: By comparing the accounts of women with DCIS to a PCC framework, we identified limitations and inconsistencies in women's lived experience of communication about DCIS, and approaches by which clinicians can more consistently achieve PCC for DCIS. Future research should develop and evaluate informational tools to support PCC 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 | 0.157 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".