Developing a tool to measure contributions to medication-related processes in family practice
Bibliographic record
Abstract
Successful team care requires a shared understanding of roles and expertise. This paper describes the development and preliminary exploration of the psychometric properties of a tool designed to measure contributions to family practice medication-related processes. Our team identified medication-related processes commonly occurring in family practice. We assessed clinical appropriateness using a sensibility questionnaire and pilot-tested with 11 pharmacists, nurses and physicians. We performed a simulated exercise to group the processes and assessed the internal consistency of the groupings using Cronbach's alpha coefficient. We examined test-retest reliability using intra-class coefficient (ICC). Following three revisions, the final Medication Use Processes Matrix (MUPM) included 22 medication-related processes and scale descriptors reflecting contribution to each process. Mean sensibility ratings were high for each component. We developed five theoretical groupings (diagnosis & prescribing, monitoring, administrative/documentation, education, medication review) and found their overall internal consistency was good (alpha > 0.80). The test-retest reliability was strong (ICC > 0.80). Preliminary validation showed significant differences in how health professionals view interprofessional contributions toward medication-related processes. Interprofessional care requires a negotiated understanding of processes and contributions. The MUPM provides an explicit description of medication-related processes in primary care, measures perceived contributions and emerges as a new tool to measure collaborative care in family practices.
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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 | 0.025 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".