A collaborative strategy with community pharmacists and physicians to improve patient experience and implement quality standards for patients with depression
Bibliographic record
Abstract
Background: The experience for patients with mental health disorders may be negatively impacted by the barriers to care, such as low health care provider-to-population ratios, travel time to reach service providers, higher hospital readmission rates, and local demand for services, especially in suburban and rural areas. Objectives: The project aimed to design a model in which physicians and pharmacists collaborate to provide comprehensive care to patients with depression in two northern communities and improve the patient and provider experience. Methods: Pharmacists and primary care physicians developed a model in which patients starting on new antidepressant medications received regular follow-up care and education on adjunct therapies from the community pharmacists instead of the physician. The patient and provider experiences were measured through surveys. Results: Out of the 14 patients who completed the patient survey, 13 reported feeling more supported by receiving follow-up care from pharmacists. Out of the 5 providers who completed the provider survey, 4 reported that the physician-pharmacist collaboration and additional support were helpful to patients. Conclusion: Overall, the project positively impacted patient experience and providers perceived value in the shared-care model.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".