Factors Shaping Pharmacists’ Adoption of Prescribing in Alberta
Bibliographic record
Abstract
Canadian pharmacists received prescribing authority in 2007 and at present, Albertan pharmacists have the broadest scope of practice in the North America. The expanded scope of practice including prescribing activities was warranted to improve healthcare services. There have been noteworthy discussions in the literature on pharmacist prescribing. However, existing literature were predominantly focused on the outcome of pharmacist prescribing and stakeholders’ perception about pharmacist prescribing in Canada. Little was known about the diffusion and adoption process of prescribing into the pharmacy practice. Therefore, the overarching objective of this thesis was to understand pharmacists’ adoption of prescribing in Alberta by applying Diffusion of Innovation (DoI) theory. To achieve this objective, we developed a conceptual model using DoI, Self-efficacy, Role belief, and Relational coordination theories and conducted five studies: 1) A scoping review to characterize existing literature on pharmacist prescribing in Canada according to research type, methodological trend, and key findings; 2) Development of a survey questionnaire to explore pharmacist prescribing adoption and establishment of the psychometric validity of the scales using factor analysis; 3) Characterizing pharmacists according to their self-reported prescribing practice using cluster analysis; 4) Exploring factors predicting pharmacist prescribing frequency and types using regression analysis; and 5) Family physicians’ experiences and perceptions of pharmacist prescribing using the Interpretive Description method. In the scoping review, we found that quantitative studies were mostly focused on measuring the outcome of pharmacist prescribing whereas; qualitative studies explored stakeholders’ perceptions. The review also suggested gaps in the evaluation of pharmacist prescribing adoption, impact on physicians’ practice, comparison of prescribing practice across provinces, and its impact on the economic system. In the second study, we developed a survey questionnaire and established the validity of five scales measuring potential predictors of pharmacist prescribing adoption – self-efficacy, prescribing belief, support from practice, impact on practice, and use of the Electronic Health Record (EHR). In the third study, we ran a secondary analysis of the survey data by applying cluster analysis and identified three major types of prescriber- “Renewal prescriber,” “Modifier”, and “Wide ranged prescriber”. The group comparisons confirmed the expected characteristics of the groups and provided evidence of the validity of the groups. In the fourth study, on exploring factors predicting pharmacist prescribing adoption, we identified practice setting, support from practice, self-efficacy, and year of experience as the significant predictors of pharmacist prescribing frequency. On the other hand, pharmacists’ practice setting and self-efficacy toward prescribing were significantly associated with the types of pharmacist prescribing adoption. In the fifth study, the qualitative exploration of family physicians’ experience and perception provided us insight on physician-pharmacist collaboration while pharmacists are adopting prescribing activities. We found three key beliefs (i.e., renewal versus initiating new prescription, community versus team pharmacist, and “I am responsible”) that shaped the physician-pharmacist prescriber collaboration. Two themes emerged from the analysis of collaboration process- trust and communication. We also found gaps in awareness and communication strategies to foster collaboration. The overall findings of this thesis suggest that features of practice setting, pharmacists’ attributes, and interprofessional collaboration with physicians shaped the pharmacist prescribing adoption in Alberta. Other jurisdictions that are planning to authorize pharmacist prescribing can reflect on our findings. Pharmacy researchers, policy-makers, and pharmacists themselves can play key roles in the successful adoption of pharmacist prescribing and improve the efficiency of health care system. Future research might evaluate the change in healthcare delivery system resulting from pharmacist prescribing as well as alterations in the relational dynamics between physician and pharmacist prescribers.
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 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".