Practice Environments and Job Satisfaction in Patient-Centered Medical Homes
Bibliographic record
Abstract
PURPOSE: We undertook a study to evaluate the effects of medical home transformation on job satisfaction in the primary care setting. METHODS: We collected primary data from 20 primary care practices participating in medical home pilot projects in Rhode Island and Colorado from 2009 to 2011. We surveyed clinicians and staff about the quality of their practice environments (eg, office chaos, communication, difficulties in providing safe, high-quality care) and job satisfaction at baseline and 30 months, and about stress, burnout, and intention to leave at 30 months. We interviewed practice leaders about the impact of pilot project participation. We assessed longitudinal changes in the practice environment and job satisfaction and, in the final pilot year, examined cross-sectional associations between the practice environment and job satisfaction, stress, burnout, and intention to leave. RESULTS: Between baseline and 30 months, job satisfaction improved in Rhode Island (P=.03) but not in Colorado. For both pilot projects, reported difficulties in providing safe, high-quality care decreased (P<.001), but emphasis on quality and the level of office chaos did not change significantly. In cross-sectional analyses, fewer difficulties in providing safe, high-quality care and more open communication were associated with greater job satisfaction. Greater office chaos and an emphasis on electronic information were associated with greater stress and burnout. CONCLUSIONS: Medical home transformations that emphasize quality and open communication while minimizing office chaos may offer the best chances of improving job satisfaction.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".