Evaluation and Implementation of ListeningTime: A Web-Based Preparatory Communication Tool for Elderly Patients With Cancer and Their Health Care Providers
Bibliographic record
Abstract
BACKGROUND: Effective patient-provider communication is an important condition to deliver optimal care and it supports patients in coping with their disease. The complex and emotionally loaded setting of oncology care challenges both health care providers (HCPs) and patients in reaching effective communication. ListeningTime is developed for elderly patients with cancer and their oncological HCPs to help them (better) prepare the clinical encounter and overcome communication barriers. ListeningTime is a Web-based preparatory communication tool including modeling videos and has an audio-facility to listen back to recorded encounters. OBJECTIVE: This study aims to evaluate the usability, perceived usefulness, and actual use of ListeningTime, through the eyes of elderly patients with cancer and their oncological HCPs. If highly rated, the ultimate goal is to make ListeningTime publicly available. METHODS: First, members of a panel of elderly cancer survivors and patients (age ≥65 years) were approached to evaluate ListeningTime through a Web-based questionnaire. The usability and perceived usefulness were assessed. Second, ListeningTime was evaluated in real-life practice through a pilot study in 3 Dutch hospitals. In these hospitals, elderly patients with cancer and their oncological HCPs were approached to evaluate ListeningTime through a similar Web-based questionnaire, measuring the perceived usefulness. In addition, we examined log files and user statistics to get insight into how the program was used. RESULTS: A total of 30 cancer survivors or patients from the patient panel, and 17 patients and 8 HCPs from the hospitals, evaluated ListeningTime. Overall, both panel members and hospital patients were positive about the ListeningTime website, audio-facility, and video fragments. Some patients suggested improvements with respect to the actors' performances in the video fragments and believed that ListeningTime is mainly suitable for non experienced patients. HCPs were also positive about ListeningTime; they valued the video fragments for patients and the audio-facility for patients and themselves. However, providers did not relisten their own recorded encounters. Patients did use the audio-facility to relisten their encounters. CONCLUSIONS: ListeningTime was highly rated, both by patients and their oncological HCPs. As a result, the video fragments of ListeningTime are now made publicly available for elderly patients with cancer through the Dutch website "kanker.nl."
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".