Development and validation of a survey instrument to measure factors that influence pharmacist adoption of prescribing in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: Study objectives were to develop a questionnaire to assess factors influencing pharmacists' adoption of prescribing (i.e., continuing, adapting or initiating therapy), describe use of pre-incentive and mixed mode survey, and establish survey psychometric properties. METHODS: Questions were developed based on prior qualitative research and Diffusion of Innovation theory. Expert review, cognitive testing, survey pilot, and main survey were used to test the questionnaire. Six content experts reviewed the questionnaire to establish face and content validity. Ten pharmacists from diverse practice settings were purposefully recruited for a cognitive interview to verify question readability. Content analysis was used to analyze the results. A pre-survey introduction letter with a monetary incentive was mailed via post to 100 (i.e. pilot) and 700 (i.e., main survey) randomly selected pharmacists. This was followed by an e-mail with a personalized link to the online questionnaire, e-mail reminders, and a telephone reminder if required. The psychometric properties of scales were evaluated with an exploratory factor analysis and Cronbach's alpha. Scale responses were described. RESULTS: Engagement of six experts and ten pharmacists clarified definitions (e.g., prescribing), terminology, recall periods, and response options for the 34-item response scale. Fifty-six pharmacists completed the online pilot survey. Based on this data, ambiguous questions and routing issues were addressed. Three hundred and seventy-eight pharmacists completed the online main survey for a response rate of 54.6%. The factors analysis resulted in 27 questions in eight scales: (1) self-efficacy, (2) support from practice environment, (3) support from interprofessional relationship, (4) impact on professionalism, (5) impact on patient care), (6) prescribing beliefs, (7) technical use of electronic health record (EHR) and (8) patient care use of the EHR. Prescribing beliefs and technical use of the EHR scales had low reliability while the remaining six scales had strong evidence for reliability and validity. CONCLUSION: Through a multi-stage process, a survey instrument was developed to capture pharmacists' perceptions of prescribing influences. This questionnaire may support future research to develop interventions to enhance adoption of prescribing and enhance direct patient care by pharmacists.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".