INvestigation of the impact of a Pharmacist in a Hospital At Home Care Team (IN PHACT)
Bibliographic record
Abstract
Background: In November 2020, Island Health, with the support of the British Columbia Ministry of Health, introduced the Hospital at Home (HaH) care model at Victoria General Hospital in Victoria, British Columbia. Given the acuity of the patients anticipated to receive care through this model, questions arose regarding how the delivery of clinical pharmacy services on which inpatients rely on could be included. With limited supporting evidence for the inclusion of a clinical pharmacist, Island Health launched the HaH program with 2 clinical pharmacists who provide services 7 days a week during daytime hours. The aim of this study is to assess the impact of the HaH pharmacist on patient care, from the perspective of the pharmacists serving in this role, patients, caregivers and program stakeholders. Methods: This prospective, observational mixed-methods study was conducted from December 2021 to March 2022. Data collection involved the HaH pharmacist documenting daily clinical activities and resolving drug therapy problems, patients and caregivers completing a 4-question postdischarge phone survey and program stakeholders completing a 9-question online survey and an optional 7-question interview. Results and Interpretation: It was found that one of the most significant roles the pharmacist plays is in identifying indications for medication therapy and making recommendations to initiate therapy where there is an absence. There was high congruence between patient, caregiver and stakeholder perceptions that the HaH pharmacist positively affects patient care within the Island Health model. Conclusion: This study provides support for the integration of a dedicated clinical pharmacist in the HaH 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".