The Performance of the Health Communication Assessment Tool© (HCAT-f) in Calibrating Different Levels of Nurse Communication Skills in a French-Speaking Context
Bibliographic record
Abstract
Communication skills training is essential in nurse education. Miscommunication may lead to adverse events and unsafe healthcare. To date, valid and reliable instruments to serve both communication training and assessment purposes across different cultural contexts are scarce. The present study empirically tested a French-language version of the Health Communication Assessment Tool© (HCAT-f) across different levels of communication skills performance to establish its reliability and validity through a cognitive fluency framework. Ten experts in communication and 52 nurse educators rated three videos simulating conversations between a nurse and a patient scheduled for lumpectomy. Each video captured a different level of communication skills performed by the nurse: High, medium, and low. Three distinct constructs were identified, i.e., professional presentation, empathy, and trust building. At absolute single-measure, an ICC = .43 suggested adequate interrater reliability of the whole scale for the medium-performed scenario, which decreased in low-performed (ICC = .35) and high-performed (ICC = .18) scenarios. The HCAT-f fulfils the criteria of linguistic equivalence, contextual relevance, and demonstrates acceptable construct validity. It can be used as a summative assessment tool after prior training on scale calibration is in place because interrater agreement was difficult to be established in high and low performance scenarios.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".