Use of Resident-Sensitive Quality Measure Data in Entrustment Decision Making: A Qualitative Study of Clinical Competency Committee Members at One Pediatric Residency
Bibliographic record
Abstract
PURPOSE: Resident-sensitive quality measures (RSQMs) are quality measures that are likely performed by an individual resident and are important to care quality for a given illness of interest. This study sought to explore how individual clinical competency committee (CCC) members interpret, use, and prioritize RSQMs alongside traditional assessment data when making a summative entrustment decision. METHOD: In this constructivist grounded theory study, 19 members of the pediatric residency CCC at Cincinnati Children's Hospital Medical Center were purposively and theoretically sampled between February and July 2019. Participants were provided a deidentified resident assessment portfolio with traditional assessment data (milestone and/or entrustable professional activity ratings as well as narrative comments from 5 rotations) and RSQM performance data for 3 acute, common diagnoses in the pediatric emergency department (asthma, bronchiolitis, and closed head injury) from the emergency medicine rotation. Data collection consisted of 2 phases: (1) observation and think out loud while participants reviewed the portfolio and (2) semistructured interviews to probe participants' reviews. Analysis moved from close readings to coding and theme development, followed by the creation of a model illustrating theme interaction. Data collection and analysis were iterative. RESULTS: Five dimensions for how participants interpret, use, and prioritize RSQMs were identified: (1) ability to orient to RSQMs: confusing to self-explanatory, (2) propensity to use RSQMs: reluctant to enthusiastic, (3) RSQM interpretation: requires contextualization to self-evident, (4) RSQMs for assessment decisions: not sticky to sticky, and (5) expectations for residents: potentially unfair to fair to use RSQMs. The interactions among these dimensions generated 3 RSQM data user profiles: eager incorporation, willing incorporation, and disinclined incorporation. CONCLUSIONS: Participants used RSQMs to varying extents in their review of resident data and found such data helpful to varying degrees, supporting the inclusion of RSQMs as resident assessment data for CCC review.
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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 | 0.048 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".