Performance of an integrated network model
Bibliographic record
Abstract
Objective To evaluate the changes in accessibility, patients’ care experiences, and quality-of-care indicators following a clinic’s transformation into a fully integrated network clinic. Design Mixed-methods study. Setting Verdun, Que. Participants Data on all patient visits were used, in addition to 2 distinct patient cohorts: 134 patients with chronic illness (ie, diabetes, arteriosclerotic heart disease, or both); and 450 women between the ages of 20 and 70 years. Main outcome measures Accessibility was measured by the number of walk-in visits, scheduled visits, and new patient enrolments. With the first cohort, patients’ care experiences were measured using validated serial questionnaires; and quality-of-care indicators were measured using biologic data. With the second cohort, quality of preventive care was measured using the number of Papanicolaou tests performed as a surrogate marker. Results Despite a negligible increase in the number of physicians, there was an increase in accessibility after the clinic’s transition to an integrated network model. During the first 4 years of operation, the number of scheduled visits more than doubled, nonscheduled visits (walk-in visits) increased by 29%, and enrolment of vulnerable patients (those with chronic illnesses) at the clinic remained high. Patient satisfaction with doctors was rated very highly at all points of time that were evaluated. While the number of Pap tests done did not increase with time, the proportion of patients meeting hemoglobin A1c and low-density lipoprotein guideline target levels increased, as did the number of patients tested for microalbuminuria. Conclusion Transformation to an integrated network model of care led to increased efficiency and enhanced accessibility with no negative effects on the doctor-patient relationship. Improvements in biologic data also suggested better quality of care.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".