Health Literacy and Nurses’ Communication With Type 2 Diabetes Patients in Primary Care Settings
Bibliographic record
Abstract
BACKGROUND: The use of the interactive communication loop has been recommended as an effective method to enhance patient understanding and recall of information. OBJECTIVE: The aim of the study was to examine the application of interactive communication loops, use of jargon, and the impact of health literacy (HL) when nurses provide education and counseling to patients with type 2 diabetes in the primary care setting in Alberta, Canada. METHODS: Encounters between nurses and patients with type 2 diabetes were audio recorded, and a patient survey including a HL measure was administered. Topics within each interaction were coded based on five key components of the communication loop and categories of jargon. RESULTS: Nine nurses participated in this study, and encounters with 36 patients were recorded. A complete communication loop was noted in only 11% of the encounters. Clarifying health information was the most commonly applied component (58% often used), followed by repeating health information (33% often used). Checking for understanding was the least applied (81% never used), followed by asking for understanding (42% never used). Medical jargon and mismatched language were often used in 17% and 25% of the encounters, respectively. Patients' HL did not materially affect patterns of communication in terms of using communication loops; however, nurses used less jargon and mismatched words with patients with inadequate HL. DISCUSSION: The overuse of medical jargon accompanied with underuse of communication loop components jeopardizes patients' comprehension and retention of information that they need to know to properly self-manage their diabetes. Nurses need to develop more effective ways to communicate concepts critical to chronic diabetes self-care education and 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".