High-performing physicians are more likely to participate in a research study: findings from a quality improvement study
Bibliographic record
Abstract
BACKGROUND: Participants in voluntary research present a different demographic profile than those who choose not to participate, affecting the generalizability of many studies. Efforts to evaluate these differences have faced challenges, as little information is available from non-participants. Leveraging data from a recent randomized controlled trial that used health administrative databases in a jurisdiction with universal medical coverage, we sought to compare the quality of care provided by participating and non-participating physicians prior to the program's implementation in order to assess whether participating physicians provided a higher baseline quality of care. METHODS: We conducted clustered regression analyses of baseline data from provincial health administrative databases. Participants included all family physicians who were eligible to participate in the Improved Delivery of Cardiovascular Care (IDOCC) project, a quality improvement project rolled out in a geographically defined region in Ontario (Canada) between 2008 and 2011. We assessed 14 performance indicators representing measures of access, continuity, and recommended care for cancer screening and chronic disease management. RESULTS: In unadjusted and patient-adjusted models, patients of IDOCC-participating physicians had higher continuity scores at the provider (Odds Ratio (OR) [95% confidence interval]: 1.06 [1.03-1.09]) and practice (1.06 [1.04-1.08]) level, lower risk of emergency room visits (Rate Ratio (RR): 0.93 [0.88-0.97]) and hospitalizations (RR:0.87 [0.77-0.99]), and were more likely to have received recommended diabetes tests (OR: 1.25 [1.06-1.49]) and cancer screening for cervical cancer (OR: 1.32 [1.08-1.61] and breast cancer (OR: 1.32 [1.19-1.46]) than patients of non-participating physicians. Some indicators remained statistically significant in the model after adjusting for provider factors. CONCLUSIONS: Our study demonstrated a participation bias for several quality indicators. Physician characteristics can explain some of these differences. Other underlying physician or practice attributes also influence interest in participating in quality improvement initiatives and existing quality levels. The standard for addressing participation bias by controlling for basic physician and practice level variables is inadequate for ensuring that results are generalizable to primary care providers and practices.
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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 | 0.026 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".