Compassionate Care in the Age of Evidence-Based Practice: A Critical Discourse Analysis in the Context of Chronic Pain Care
Bibliographic record
Abstract
PURPOSE: Health professions education and practice have seen renewed calls to restore compassion to care. However, because of the ways evidence-based practice (EBP) has been implemented in health care, wherein research-based knowledge is privileged, the dominance of EBP may silence clinician and patient experience-based knowledge needed for compassionate care. This study explored what happens when the discourses of compassionate care and EBP interact in practice. METHOD: Chronic pain management in Canada was selected as the context for the study. Data collection involved compiling an archive of 458 chronic pain texts, including gray literature from 2009-2015 (non-peer-reviewed sources, e.g., guidelines), patient blog posts from 2013-2015, and transcripts of study interviews with 9 clinicians and postgraduate trainees from local pain clinics from 2015-2016. The archive was analyzed using an interpretive qualitative approach informed by critical discourse analysis. RESULTS: Four manifestations of the discourse of compassionate care were identified: curing the pain itself, returning to function, alleviating suffering, and validating the patient experience. These discourses produced particular subject positions, activities, practices, and privileged forms of knowledge. They operated in response, partnership, apology, and resistance, respectively, to the dominant discourse of EBP. These relationships were mediated by other prevalent discourses in the system: patient safety, patient-centered care, professional liability, interprofessional collaboration, and efficiency. CONCLUSIONS: Medical education efforts to foster compassion in health professionals and systems need to acknowledge the complex web of discourses-which carry with them their own expectations, material effects, and roles-and support people in navigating this web.
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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 | 0.047 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.025 | 0.054 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| 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".