myTIPreport and Training for Independent Practice: A Tool for Real-Time Workplace Feedback for Milestones and Procedural Skills
Bibliographic record
Abstract
ABSTRACT Background Few tools currently exist for effective, accessible delivery of real-time, workplace feedback in the clinical setting. Objective We developed and implemented a real-time, web-based tool for performance-based feedback in the clinical environment. Methods The tool (myTIPreport) was designed for performance-based feedback to learners on the Accreditation Council for Graduate Medical Education (ACGME) Milestones and procedural skills. “TIP” stands for “Training for Independent Practice.” We implemented myTIPreport in obstetrics and gynecology (Ob-Gyn) and female pelvic medicine and reconstructive surgery (FPMRS) programs between November 2014 and May 2015. Residents, fellows, teachers, and program directors completed preimplementation and postimplementation surveys on their perceptions of feedback. Results Preimplementation surveys were completed by 656 participants of a total of 980 learners and teachers in 19 programs (12 Ob-Gyn and 7 FPMRS). This represented 72% (273 of 378) of learners and 64% (383 of 602) of teachers. Seventy percent of participants (381 of 546) reported having their own individual processes for real-time feedback; the majority (79%, 340 of 430) described these processes as informal discussions. Over 6 months, one-third of teachers and two-thirds of learners used the myTIPreport tool a total of 4311 times. Milestone feedback was recorded 944 times, and procedural feedback was recorded 3367 times. Feedback addressed all ACGME Milestones and procedures programmed into myTIPreport. Most program directors reported that tool implementation was successful. Conclusions The majority of learners successfully received workplace feedback using myTIPreport. This web-based tool, incorporating procedures and ACGME Milestones, may be an important transition from other feedback formats.
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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 | 0.022 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".