Strategies for effective goals of care discussions and decision-making: perspectives from a multi-centre survey of Canadian hospital-based healthcare providers
Bibliographic record
Abstract
BACKGROUND: Communication gaps impact the quality of patient care. Previous research has focused on communication barriers rather than seeking solutions. Our aim was to identify strategies for effective communication and decision-making about goals of care for medical interventions in serious illness, from the perspectives of hospital-based healthcare providers. METHODS: A cross-sectional survey composed of closed- and open-ended questions about goals of care communication and decision-making was administered to healthcare providers in 13 centres in six Canadian provinces. We analyzed a portion of the open-ended survey questions, specifically (1) suggestions for overcoming barriers encountered in discussing goals of care, and (2) currently effective practices. Thematic content analysis was used to analyze responses to the open-ended questions. RESULTS: Of the 1,256 respondents to the larger survey, 468 responded to the open-ended questions (37%), including 272 of 512 nurses (53%), 153 of 484 internal medicine trainees (32%), and 43 of 260 attending physicians (17%). Responses to each of the two questions were similar, generating a common set of themes and subthemes. Effective strategies and ideas for improving communication and decision-making about goals of care clustered under five themes: patient and family factors, communication between healthcare providers and patients, interprofessional collaboration, education, and resources. Subthemes highlighted core elements of shared decision-making. CONCLUSIONS: Translating our findings into multifaceted interventions that consider patient and family factors, address knowledge gaps, optimize resource utilization, and facilitate communication and collaboration between patients, families and healthcare providers may improve communication and decision-making about goals of care.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".