Improving Undergraduate Medical Education about Pain Assessment and Management: A Qualitative Descriptive Study of Stakeholders’ Perceptions
Bibliographic record
Abstract
BACKGROUND: Pain is one of the most common reasons for individuals to seek medical advice, yet it remains poorly managed. One of the main reasons that poor pain management persists is the lack of adequate knowledge and skills of practicing clinicians, which stems from a perceived lack of pain education during the training of undergraduate medical students. OBJECTIVE: To identify gaps in knowledge with respect to pain management as perceived by students, patients and educators. METHODS: A qualitative descriptive study was conducted. Data were generated through six focus groups with second- and fourth-year medical students, four focus groups with patients and individual semistructured interviews with nine educators. All interviews were audiotaped and an inductive thematic analysis was performed. RESULTS: A total of 70 individuals participated in the present study. Five main themes were identified: assessment of physical and psychosocial aspects of pain; clinical management of pain with pharmacology and alternative therapies; communication and the development of a good therapeutic relationship; ethical considerations surrounding pain; and institutional context of medical education about pain. CONCLUSION: Participating patients, students and pain experts recognized a need for additional medical education about pain assessment and management. Educational approaches need to teach students to gather appropriate information about pain, to acquire knowledge of a broad spectrum of therapeutic options, to develop a mutual, trusting relationship with patients and to become aware of their own biases and prejudice toward patients with pain. The results of the present study should be used to develop and enhance existing pain curricula content.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".