MétaCan
Menu
Back to cohort
Record W4221105278 · doi:10.1016/j.rcsop.2022.100125

A collaborative strategy with community pharmacists and physicians to improve patient experience and implement quality standards for patients with depression

2022· article· en· W4221105278 on OpenAlexaff
Anastasia Shiamptanis, Jenn Osesky, Joanna de Graaf-Dunlop

Bibliographic record

VenueExploratory Research in Clinical and Social Pharmacy · 2022
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsHealth Canada
Fundersnot available
KeywordsMedicineFeelingFamily medicinePharmacistMental healthNursingCollaborative CareDepression (economics)Health carePatient experiencePopulationPharmacyPrimary carePsychiatryPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.331
GPT teacher head0.619
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueExploratory Research in Clinical and Social PharmacySame topicMental Health Treatment and AccessFrench-language works237,207