Reshaping Orthopaedic Resident Education in Systems-Based Practice
Bibliographic record
Abstract
BACKGROUND: Despite advances in understanding the "systems-based practice" competency in resident education, this topic has remained difficult to teach, assess, and document. The goal of this study was to perform a needs assessment and an analysis of the current state of systems-based practice education in orthopaedic residency programs across the U.S. and within our own institution. METHODS: A sample of orthopaedic educators and residents from across the U.S. who were attending the 2010 American Orthopaedic Association (AOA) Effective Orthopaedic Educator Course, AOA Resident Leadership Forum, and AOA Council of Residency Directors meeting were surveyed to determine (1) which aspects of systems-based practice, if any, were being taught; (2) how systems-based practice is being taught; and (3) how residency programs are assessing systems-based practice. In addition, an in-depth case study of these issues was performed by means of seven semi-structured focus group sessions with diverse stakeholders who participated in the care of musculoskeletal patients at the authors' institution. A quantitative approach was used to analyze the survey data. The focus group data were analyzed with procedures associated with grounded theory, relying on a constant comparative method to develop salient themes arising from the discussion. RESULTS: "Clinical observation" (33%) and "didactic case-based learning" (23%) were reported by the survey respondents as the most commonly used teaching methods, but specific topics were taught inconsistently. Competency assessment was reported to occur infrequently, and 36% of respondents reported that systems-based practice areas were not being assessed by any methods. The focus group discussions emphasized the need for standardized experiential learning that was closely linked to the patient's perspective. Orthopaedic faculty members were uncomfortable with their knowledge of this competency and their ability to teach and assess it. CONCLUSIONS: Teaching the systems-based practice competency occurs inconsistently, and formal assessment occurs infrequently. In addition to formal teaching, learning systems-based practice will be best achieved experientially and from the patient's perspective.
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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 | 0.015 | 0.020 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".