I am <i>ready</i> to see you now, Doctor! A mixed‐method study of the Let's Discuss Health website implementation in Primary Care
Bibliographic record
Abstract
BACKGROUND: Let's Discuss Health (LDH) is a website that encourages patients to prepare their health-care encounters by providing communication training, review of topics and questions that are important to them. OBJECTIVE: To describe LDH implementation during primary care (PC) visits for chronic illnesses. METHODS: Design: Descriptive mixed-method study. SETTING: 6 PC clinics. PARTICIPANTS: 156 patients and 51 health-care providers (HCP). INTERVENTION: LDH website implementation. OUTCOME MEASURES: Perceived quality and usefulness of LDH; perceived quality of HCP-patient communication; patient activation; LDH integration in routine PC practices and barriers to its use. RESULTS: Patients reported a positive perception of the website in that it helped them to adopt an active role in the encounters; recall their visit agenda and reduce encounter-related stress; feel more confident to ask questions, feel more motivated to prepare their future medical visits and improve their chronic illness management. However, a certain disconnect emerged between HCP and patient perceptions as to the value of LDH in promoting a sense of partnership and collaboration. The main barriers to the use of LDH are HCP lack of interest, limited access to technology, lack of time and language barriers. CONCLUSION: Our findings indicate that it is advantageous for patients to prepare their medical encounters. However, the study needs to be replicated in other medical environments using larger and more diverse samples. PATIENT AND PUBLIC CONTRIBUTION: Patient partners were involved in the conduct of this 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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".