[Impact of standing order prescriptions on the joint follow-up of diabetics in primary care: a case study].
Bibliographic record
Abstract
The burden of chronic disease requires a new organization of medical care and services. Enhancing collaboration among front-line care givers facilitates access to care and optimizes follow-up. As a result, a new organizational structure has been gradually deployed in Quebec since 2003. Family Medicine Groups (FMGs) use a new type of standing order, prescribing details of care. Among 52 FMGs surveyed, an exemplarygroup was identified that most successfully instituted more and higher-impact standing orders. This single case study explored the impact of standing orders used for diabetes follow-up on professional practices, physician-nurse-patient interactions and patient self-management. The data collected and analyzed were derived from more than 200 documents, 15 hours of observation in the clinic, and individual interviews of ten patients, three nurses and eight doctors. Standing ordersformalizing thejointfollow-up ofdiabetic patients both increased professional collaboration and improved patient-professional interactions. As professionals and patients achieved a better consensus, patient self-management was improved. Ultimately, for professionals, standing orders facilitate a better match between the use of their time and skills, and their aspirationsfor practice. Patients are reassured and empowered by ready access to care and their progress in self-management skills. Concrete measures, such as standing orders, modify care delivery by reinforcing professional collaboration, and facilitate patient self-care, in accordance with the Chronic Care Model (CCM).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".