Medical Trainees’ Experiences of Treating People With Chronic Pain: A Lost Opportunity for Medical Education
Bibliographic record
Abstract
PURPOSE: Evidence suggests that physicians' opinions about patients with chronic pain become progressively negative over the course of medical training, leading to decline in empathy for these patients. Few qualitative studies have focused on this issue, and thus the experiences shaping this process remain unexplored. This study addressed how medical trainees learn about chronic pain management through informal and formal curricula. METHOD: This study adopted a constructive qualitative approach informed by the theoretical lens of the hidden curriculum. Thirteen open-ended interviews were conducted with medical students and residents at various training stages; interviewees had experience treating patients with chronic pain, shadowing the care of these patients, or both. Interviews elicited information about stage of medical training, general descriptions of work, and concrete experiences of managing patients with chronic pain. All interviews were collected in Toronto between June and August 2015. RESULTS: Most interviewees described the management of chronic pain as challenging and unrewarding and attributed this at least in part to their perception that pain was subjective. Trainees also recounted that their inability to cure chronic pain left them confused about how to provide care, and voiced a perception that preceptors seemed to view these patients as having little educational value. CONCLUSIONS: Specifically because chronic pain is subjective and incurable, listening and communication become crucial for patient care. Instead of sheltering trainees, medical educators should be offered the opportunity to reflect on the skills that are required to provide patient-centered care for this population. This approach has the potential to greatly benefit both trainees and patients.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".