Assessment of Pediatricians by a Regulatory Authority
Bibliographic record
Abstract
OBJECTIVE: To determine whether it is possible to develop feasible, valid, and reliable multisource feedback data for pediatricians. METHODS: Surveys with 40, 22, 38, and 37 items were developed for assessment of pediatricians by patients, co-workers, medical colleagues, and themselves, respectively, using 5-point scales with an "unable to assess" category. Items addressed key competencies related to communication skills, professionalism, collegiality, continuing professional development, and collaboration. Each pediatrician was assessed by 25 patients, 8 medical colleagues, and 8 co-workers. Feasibility was assessed with response rates for each instrument. Validity was assessed with rating profiles, the percentage of participants unable to assess the physician for each item, and exploratory factor analyses to determine which items grouped together into scales. Cronbach's alpha and generalizability coefficient analyses assessed reliability. RESULTS: One hundred pediatricians participated. The mean number of respondents per physician was 23.4 (93.6%) for patients, 7.6 (94.8%) for co-workers, and 7.6 (95.5%) for medical colleagues. The mean ratings ranged from 4 to 5 for each item on each scale. Few items had high percentages of "unable to assess" responses. The factor analyses revealed a 4-factor solution for the patient survey, a 3-factor solution for the co-worker survey, and a 4-factor solution for the medical colleague survey, accounting for at least 64% of the variance. All instruments had high internal consistency. The generalizability coefficients were .85 for patients, .87 for co-workers, and .78 for medical colleagues. CONCLUSION: Surveys can be developed to provide feedback data on key competencies.
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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 | 0.047 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".