Pharmacists’ perceptions of their working conditions and the factors influencing this: Results from 5 Canadian provinces
Bibliographic record
Abstract
INTRODUCTION: Our previous study in British Columbia (BC) indicated that pharmacists have a poor perception of their working conditions. The objective of this study is to assess pharmacists' perceptions of their working conditions in 4 other Canadian provinces. METHODS: This was a cross-sectional study across Alberta, New Brunswick, Prince Edward Island and Newfoundland and Labrador, using a survey adapted from the Oregon Board of Pharmacy. Data collected previously from BC were also included in the analyses. The survey was emailed to all pharmacist registrants. Respondents were provided with 6 statements and asked to rate their agreement with them, using a 5-point Likert scale. Statements were framed such that agreement with them indicated good perception of working conditions. Logistic regression analyses were used to study the relationship between workplace factors on perception of working conditions. RESULTS: Pharmacists perceived their working conditions to be poor. Pharmacists indicated that they do not have time for break/lunch (48.3% of respondents), work in environments that are not conducive to safe and effective primary care (26.5%), are not satisfied with the amount of time they have to do their job (44.0%) and face shortage of staff (shortage of pharmacists: 33.7%, technicians: 36.4%, clerk staff: 30.3%). Significant factors associated with poor perception were workplace-imposed quotas, high prescription volume, working in chain pharmacies and long prescription wait times. CONCLUSION: A high percentage of Canadian pharmacists perceived their working conditions to be poor. Considering the patient-related consequences of pharmacists' poor working conditions and the system-related reasons identified behind it, we call for collaborative efforts to tackle this issue.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".