The Causal Relationship between Interns' Knowledge and Self-Efficacy and Their Value in Predicting the Interns' Communication Behavior with Patients.
Bibliographic record
Abstract
BACKGROUND: After many years of teaching, both the efficiency and efficacy of communication skills programs are under question because patients' dissatisfaction with doctors' communication behavior is at the top of the complaint lists. It is assumed that finding the specific role of different determinants of doctors' communication behavior, instructional designers can plan more effective training programs. This study aims to explore the predictive value of interns' knowledge and self-efficacy in building effective relationship with patients and determine the causal relationship between interns' knowledge and self-efficacy about effective doctor-patient relationship. METHODS: In this cross-sectional study, PRECEDE model was applied and the analyzed content from semi-structured interviews with 7 interns and 14 faculty members was combined with the items from literature review. All the emerged items were categorized under eight constructs of social cognitive theory. The validity and reliability of the items of the research questionnaire were examined by 40 interns and an expert panel of 14 faculty members. The questionnaires were completed by 203 medical interns and confirmatory factor analysis (CFA) was done on the items. The data were analyzed by SPSS.21 and LISREL 8.80. RESULTS: CFA indicated a good fit to the data. Knowledge and self-efficacy, together, explained 23 percent of the variance in interns' communicative behavior. 53 percent of the changes in interns' self-efficacy were attributed to the changes in interns' knowledge. CONCLUSION: Improving the interns' shared vision can increase the quality of their knowledge and instructional designs based on learning facts, and gaining insights about effective doctor-patient relationship can increase the interns' self-efficacy and consequently improve the interns' communication skills.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".