Self-directed learning needs, patterns, and outcomes among general surgeons
Bibliographic record
Abstract
INTRODUCTION: To explore the relationship between self-directed learning (SDL) needs, patterns, barriers, and outcomes among nonacademic general surgeons. METHODS: Participants dictated details of SDL episodes associated with cancer patient management from October 2007 to March 2008. Transcripts were coded thematically. Frequencies were calculated for elements of each SDL stage. Statistical significance among subgroups was established with the use of the Pearson chi-square test, adjusted for clustering by surgeon. Participants were interviewed by telephone, and transcripts were analyzed by qualitative methods. RESULTS: Of 21 consenting surgeons, 15 submitted 115 cases, and 108 were analyzed. Most involved breast (40.7%), colon (18.5%), or rectal cancer (13.0%); 2 or more clinical tasks (41.7%); and 2 or more questions (89.8%). Information was sought from the Internet (48.1%), colleagues (24.2%), or both (6.8%). Information was partially, or not relevant for 21.3% of cases. Evidence was new for 66.7%, and confirmed knowledge for 10.7% of cases. Learning helped surgeons formulate new (34.2%), or confirm original (16.5%) management plans, or determine that referral was appropriate (39.2%). Use of codified sources was associated with information retrieval (P < .05), and identifying new evidence leading to a change in management from that initially proposed (P < or = .001). DISCUSSION: Numerous individual and systemic barriers may prevent practicing physicians from undertaking SDL, but provision of structured guidance prompted SDL and resulted in several beneficial outcomes. Further research is needed to validate these findings, and investigate who should support SDL, and how.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".