Exploring the Predictive Ability of Two New Complementary Instruments for Assessing Effective Therapeutic Communication Skills of Dental and Dental Hygiene Students
Bibliographic record
Abstract
Research on the development of effective therapeutic communication skills for oral health providers is slowly evolving. One of the initial steps in this research is to identify and address gaps in the work of previous researchers. Ultimately, the educational goal of competence in communications skills development is to provide improved patient care including improved patient satisfaction. This article is the third in a series describing the development of and findings from the new complementary Patient Communication Assessment Instrument (PCAI) and Student Communication Assessment Instrument (SCAI). The aim of the study reported here was to look at the relationship between communication skills and patient and student clinician gender interactions, sociodemographic factors (e.g., age, income), and changes in these interactions with length of treatment. A total of 410 patient assessments (PCAI) and 410 matching student self-assessments (SCAI) were used for further data analysis. Patients of female student clinicians, female patients, patients of a higher and the lowest income range, and older patients reported statistically significant higher student communication scores. The PCAI identified that certain groups of patients consistently report higher scores than other groups, whereas the SCAI identified differences between male and female student clinicians. The results have implications for educational protocols, communication strategies, and the need for continued research regarding sociodemographic factors and their relationship to patient satisfaction.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".