EXACKTE2: Exploiting the clinical consultation as a knowledge transfer and exchange environment: a study protocol
Bibliographic record
Abstract
BACKGROUND: While the evidence suggests that the way physicians provide information to patients is crucial in helping patients decide upon a course of action, the field of knowledge translation and exchange (KTE) is silent about how the physician and the patient influence each other during clinical interactions and decision-making. Consequently, based on a novel relationship-centered model, EXACKTE(2) (EXploiting the clinicAl Consultation as a Knowledge Transfer and Exchange Environment), this study proposes to assess how patients and physicians influence each other in consultations. METHODS: We will employ a cross-sectional study design involving 300 pairs of patients and family physicians from two primary care practice-based research networks. The consultation between patient and physician will be audio-taped and transcribed. Following the consultation, patients and physicians will complete a set of questionnaires based on the EXACKTE(2) model. All questionnaires will be similar for patients and physicians. These questionnaires will assess the key concepts of our proposed model based on the essential elements of shared decision-making (SDM): definition and explanation of problem; presentation of options; discussion of pros and cons; clarification of patient values and preferences; discussion of patient ability and self-efficacy; presentation of doctor knowledge and recommendation; and checking and clarifying understanding. Patients will be contacted by phone two weeks later and asked to complete questionnaires on decisional regret and quality of life. The analysis will be conducted to compare the key concepts in the EXACKTE(2) model between patients and physicians. It will also allow the assessment of how patients and physicians influence each other in consultations. DISCUSSION: Our proposed model, EXACKTE(2), is aimed at advancing the science of KTE based on a relationship process when decision-making has to take place. It fosters a new KTE paradigm by putting forward a relationship-centered perspective and has the potential to reveal unknown mechanisms that underline effective KTE in clinical contexts. This will result in better understanding of the mechanisms that may promote a new generation of knowledge transfer strategies.
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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 | 0.046 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".