Constructing a Communication Scale to Measure the Effectiveness of Interprofessional Communication
Bibliographic record
Abstract
Introduction In our modern healthcare, healthcare teams are becoming increasingly multidisplinary which brings upon the need for them to be effective in order to have better patient outcomes. Communication failures can occur in interprofessional healthcare teams which leads to negative health outcomes. In order to evaluate communication in interprofessional groups, a communication scale is needed. Aim Given that there is currently no validated communication scale for interprofessional groups, the study's goal was to create and validate a communication scale in the context of an interprofessional based dissection course. Methods Observations of the 34 students in the dissection study (N = 34) were conducted for the first 3 classes. With the use of these observations and prior qualitative analysis of focus group interviews from the course, a communication scale was constructed. The question items were then vetted by coordinators of the dissection course and research experts which led to the communication scale being shortened from 54 questions to 28 questions. The communication scale was used by each student in the class to assess themselves and two of their peers in their group resulting in 3 scores altogether. This procedure was repeated one week later to evaluate the reliability of the scale. Results There is a 0.331 Pearson's Correlation between peer rater 1 and the self‐rater, a 0.139 Pearson's Correlation between peer rater 2 and the self‐rater and a 0.279 Pearson's Correlation between peer rater 1 and 2. These results indicate a significant difference between self and peer rating, and only a moderate correlation between any two of the three raters. Also, the large standard deviation associated with each of the 3 scores is indicative of large variability of scores around the mean total score. Discussion and Conclusion Overall, it is clear from the results that the constructed communication scale is not sufficiently reliable. Therefore, the next steps of the study is to improve reliability of the scale. This is by performing factor analysis to see whether question items can be decreased and grouped together to create subscales. Then, the subscales will be tested to see whether they are effective in measuring changes in communication skills of interprofessional groups over the duration of the course. Student feedback on the communication scale will also be used to improve on the effectiveness and reliability of the scale. Support or Funding Information N/A This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".