Medication discussion between nurse prescribers and people with diabetes: an analysis of content and participation using MEDICODE
Bibliographic record
Abstract
AIM: This paper is a report of a study to identify the content of, and participation in, medicine discussion between nurse prescribers and people with diabetes in England. BACKGROUND: Diabetes affects 246 million people worldwide and effective management of medicines is an essential component of successful disease control. There are now over 20,000 nurse independent prescribers in the UK, many of whom frequently prescribe for people with diabetes. With this responsibility comes a challenge to effectively communicate with patients about medicines. National guidelines on medicines communication have recently been issued, but the extent to which nurse prescribers are facilitating effective medicine-taking in diabetes remains unknown. METHODS: A purposive sample of 20 nurse prescribers working with diabetes patients audio-recorded 59 of their routine consultations and a descriptive analysis was conducted using a validated coding tool: MEDICODE. Recordings were collected between January and July 2008. The unit of analysis was the medicine. RESULTS: A total of 260 instances of medicine discussion identified in the audio-recordings were analysed. The most frequently raised themes were 'medication named' (raised in 88·8% of medicines), 'usage of medication' (65·4%) and 'instructions for taking medication' (48·5%). 'Reasons for medication' (8·5%) and 'concerns about medication' were infrequently discussed (2·7%). Measures of consultation participation suggest largely dyadic medicine discussion initiated by nurse prescribers. CONCLUSION: MEDICODE discussion themes linked to principles of recent guidelines for effective medicine-taking were infrequently raised. Medicine discussion was characterized by a one statement-one response style of communication led by nurses. Professional development is required to support theoretically informed approaches to effective medicines management.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".