Predictors of Pharmacy Students’ Intentions to Monitor Diabetes
Bibliographic record
Abstract
PURPOSE: This research explores predictors of pharmacy students' adoption of one specific behavior, monitoring diabetes ABCs (A1c, blood pressure, and cholesterol) in the community pharmacy. Specifically, this research assessed which student situation and attitudinal factors are predictors of students' intentions and behavior in asking patients about the diabetes targets and goals as per a conceptual model. METHODS: Data was drawn from a randomized controlled trial to assess the impact of the diabetes check in pharmacy students during their community pharmacy clerkships. A survey measured students' self-efficacy, outcome expectancies, role beliefs, mattering as well as students' experiences with the Diabetes Check and intentions to routinely monitor diabetes. Stepwise hierarchical multiple linear regression reflected the conceptual model and was used to assess the research questions. RESULTS: Survey response rate was 94% and analysis was performed on a sample of 118 students. In summary, pharmacy students' intentions and monitoring behaviors were predicted by the students' situation and attitudes. Specifically, students' intentions to ask patients about the diabetes ABCs were predicted by pharmacy site counseling, monitoring role beliefs, self-efficacy, and positive outcome expectancies. Mattering predicted intentions, but differently in each study group. Behavior in asking about patients with diabetes about blood pressure and cholesterol was predicted by pharmacy site counseling, self efficacy, and monitoring role beliefs. Students' behavior in asking about A1c was pharmacy site counseling, self efficacy, and monitoring role beliefs in additional to completing the Diabetes Check assignment. CONCLUSIONS: Monitoring intentions and behaviors were consistently predicted by pharmacy site counseling, monitoring role beliefs, and self-efficacy and future research investigating the pharmacists' behavior should include these variables. The role of mattering and outcome expectancies in predicting monitoring intentions requires further study.
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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 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".