Patient care activities by community pharmacists in a capitation funding model mental health and addictions program
Bibliographic record
Abstract
BACKGROUND: Community pharmacists are autonomous, regulated health care professionals located in urban and rural communities in Canada. The accessibility, knowledge, and skills of community pharmacists can be leveraged to increase mental illness and addictions care in communities. METHODS: The Bloom Program was designed, developed, and implemented based on the Behaviour Change Wheel and a program of research in community pharmacy mental healthcare capacity building. We evaluated the Bloom Program as a demonstration project using mixed methods. A retrospective chart audit was conducted to examine outcomes and these are reported in this paper. RESULTS: We collected 201 patient charts from 23 pharmacies in Nova Scotia with 182 patients having at least one or more follow-up visits. Anxiety (n = 126, 69%), depression (n = 112, 62%), and sleep disorders (n = 64, 35%) were the most frequent mental health problems. Comorbid physical health problems were documented in 57% (n = 104). The average number of prescribed medications was 5.5 (range 0 to 24). Sixty seven percent (n = 122) were taking multiple psychotropics and 71% (n = 130) reported taking more than one medication for physical health problems. Treatment optimization was the leading reason for enrollment with more than 80% seeking improvements in symptom management and daily functioning. There were a total of 1233 patient-care meetings documented, of which the duration was recorded in 1098. The median time for enrolling, assessing, and providing follow-up care by pharmacists was 142 min (mean 176, SD 128) per patient. The median follow-up encounter duration was 15 min. A total of 146 patient care encounters were 60 min or longer, representing 13.3% of all timed encounters. CONCLUSIONS: Pharmacists work with patients with lived experience of mental illness and addictions to improve medication related outcomes including those related to treatment optimization, reducing polytherapy, and facilitating withdrawal from medications. Pharmacists can offer their services frequently and routinely without the need for an appointment while affording patient confidentiality and privacy. Important roles for pharmacists around the deprescribing of various medications (e.g., benzodiazepines) have previously been supported and should be optimized and more broadly implemented. Further research on the best mechanisms to incentivize pharmacists in mental illness and addiction's care should be explored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".