Pharmacists report lack of reinforcement and the work environment as the biggest barriers to routine monitoring and follow-up for people with diabetes: A survey of community pharmacists
Bibliographic record
Abstract
BACKGROUND: Medications with lifestyle are the cornerstone of diabetes management and routine monitoring and follow-up are essential to the delivery of quality care. Documented follow-up rates by pharmacists for people with diabetes are low despite good uptake of initial medication assessments in medication review programs. OBJECTIVES: Identify the barriers and facilitators to routine monitoring and follow-up for people with diabetes by community pharmacists. METHODS: Pharmacists were invited to complete a survey designed using the Theoretical Domains Framework Version 2 TDF (v2) consisting of 39 questions based on the 14 domains of the TDFv2 with quantitative response options using a 7 point Likert scale and 2 open-ended questions. Baseline information about the respondents and their practice sites were summarized using descriptive statistics. Mean scores and standard deviations were calculated for each of the Likert scale responses. Responses to open-ended questions were analyzed and coded using an inductive thematic approach. RESULTS: 346 pharmacists completed the survey (4.76% response rate). The TDF domains found to be positively influencing the delivery of routine monitoring and follow-up activities were beliefs about consequences for people with diabetes (6.08 ± 1.13), pharmacist knowledge (5.93 ± 0.99), pharmacist skills (5.44 ± 1.44), social influences (5.36 ± 1.32) and optimism (5.20 ± 1.58). The domains found to be negatively influencing were reinforcement (3.0 ± 1.89) and environmental context and resources (3.30 ± 1.81). Themes emerging from the thematic analysis included time and competing priorities, reimbursement, patient engagement, workflow and human resources, access to labs and clinical information, information technology and support from the owner/manager. CONCLUSIONS: Our research concludes that pharmacists report that their knowledge, skills, and beliefs about their role and responsibility, social influences and optimism are positive influences on routine monitoring and follow-up while reinforcement and the environmental context/resources are the greatest negative influences. Strategies to improve follow-up should be focused in these areas.
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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".