Poor Communication in Cancer Care
Bibliographic record
Abstract
BACKGROUND: Communication in cancer care is a recognized problem for patients. Research to date has provided limited relevant knowledge toward solving this problem. OBJECTIVE: Our research program aims to understand helpful and unhelpful communication from the patient perspective and to document changes in patient needs and priorities over time. In this analysis, we focus on patient perceptions of poor communication. METHODS: Using a qualitative longitudinal approach informed by interpretive description methodology, we are following a cohort of adult cancer patients across their cancer journey. We used constant comparative analysis of repeated interviews to examine thematic patterns in their perceptions and interpret both commonalities and diversities. RESULTS: Patient accounts reveal 3 types of poor communication. "Ordinary misses" are everyday missteps for which maturation and socialization may be an adequate solution. "Systemic misunderstandings" are assumptive gaps between patients and professionals, which may be addressed through qualitative research. "Repeat offenders" are a subset of clinicians whose communication patterns become a particular source of patient distress. CONCLUSIONS: This typology offers a novel way to conceptualize the problem of poor communication in cancer care toward more effective solutions for the communication problem. Managing the communication of a problematic subset of clinicians will likely require strategic interventions at the level of organizational culture and models of care. IMPLICATIONS FOR PRACTICE: Nurses can play a meaningful role in detecting and buffering sources of poor communication in the practice context. Addressing poor communication may be a further reason to advocate for interprofessional team-based care models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".