Use of chronic disease management programs for diabetes
Bibliographic record
Abstract
Objective To determine the types of chronic disease management (CDM) programs offered for patients with diabetes in Alberta's primary care networks (PCNs). Design A survey was administered to PCNs to determine the types of CDM programs offered for patients with diabetes; CDM programs were organized into categories by their resource intensity and effectiveness. Results of the survey were reported using frequencies and percentages. Setting Alberta has recently created PCNs—groups of family physicians who receive additional funds to enable them to support activities that fall outside the typical physician-based fee-for-service model, but which address specified objectives including CDM. It is currently unknown what additional programs are being provided through the PCN supplemental funding. Participants A survey was administered to the individual responsible for CDM in each PCN. This included executive directors, chronic disease managers, and CDM nurses. Main outcome measures We determined the CDM strategies used in each PCN to care for patients with diabetes, whether they were available to all patients, and whether the services were provided exclusively by the PCN or in conjunction with other agencies. Results There was considerable variation across PCNs with respect to the CDM programs offered for people with diabetes. Nearly all PCNs used multidisciplinary teams (which could include nurses, dietitians, and pharmacists) and patient education. Fewer than half of the PCNs permitted personnel other than the primary physician to write or alter prescriptions for medications. Conclusion Alberta's PCNs have successfully established many different types of CDM programs. Multidisciplinary care teams, which are among the most effective CDM strategies, are currently being used by most of Alberta's PCNs.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".