Feedback data sources that inform physician self-assessment
Bibliographic record
Abstract
BACKGROUND: Self-assessment is a process of interpreting data about one's performance and comparing it to explicit or implicit standards. AIM: To examine the external data sources physicians used to monitor themselves. METHODS: Focus groups were conducted with physicians who participated in three practice improvement activities: a multisource feedback program; a program providing patient and chart audit data; and practice-based learning groups. We used grounded theory strategies to understand the external sources that stimulated self-assessment and how they worked. RESULTS: Data from seven focus groups (49 physicians) were analyzed. Physicians used information from structured programs, other educational activities, professional colleagues, and patients. Data were of varying quality, often from non-formal sources with implicit (not explicit) standards. Mandatory programs elicited variable responses, whereas data and activities the physicians selected themselves were more likely to be accepted. Physicians used the information to create a reference point against which they could weigh their performance using it variably depending on their personal interpretation of its accuracy, application, and utility. CONCLUSIONS: Physicians use and interpret data and standards of varying quality to inform self-assessment. Physicians may benefit from regular and routine feedback and guidance on how to seek out data for self-assessment.
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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 | 0.114 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".