Teaching Chronic Pain in the Family Medicine Clerkship: Influences of Experience and Beliefs About Treatment Effectiveness: A CERA Study.
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a common and important disease state in North America, but many medical students and practicing physicians feel poorly prepared to treat this condition. METHODS: Data were collected via the 2014 CERA Family Medicine Clerkship Director survey, which was electronically sent to 121 US and 16 Canadian clerkship directors. The authors sought to determine the quantity of chronic pain management instruction included in clerkship curricula and any characteristics of clerkship directors that correlated with the teaching of various pain topics. Survey items included the total amount of time spent teaching about chronic pain, various subtopics addressed, and personal characteristics of clerkship directors (years as clerkship director, number of years since graduation, amount of pain-related CME taken yearly, confidence in caring for patients with chronic pain, and belief in efficacy of various treatments). RESULTS: The response rate was 91%. Half of respondents indicated that they do not teach about chronic pain during the clerkship at all. The mean number of minutes spent teaching about chronic pain during the family medicine clerkship was 48 minutes (SD=65.). The majority of clerkship directors felt confident about their ability to treat chronic pain, and there was a positive correlation between confidence and time teaching about chronic pain during the family medicine clerkship. Confidence in treating chronic pain patients also correlated with the likelihood of covering several specific pain subtopics, including pain assessment, documentation skills, non-pharmacologic treatment, treatment with opioids, and treatment with non-opioids. CONCLUSIONS: Chronic pain management is currently taught in only about half of family medicine clerkships. Confidence in caring for chronic pain patients is the only characteristic of clerkship directors that predicts whether the subject of chronic pain will be taught within the family medicine clerkship.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".