Patients and Caregivers Rate the PAINReportIt Wireless Internet-Enabled Tablet as a Method for Reporting Pain During End-of-Life Cancer Care
Bibliographic record
Abstract
BACKGROUND: In several studies, investigators have successfully used an internet-enabled PAINReportIt tablet to allow patients to report their pain to clinicians in real-time, but it is unknown how acceptable this technology is to patients and caregivers when used in their homes. OBJECTIVE: The aims of this study were to examine computer use acceptability scores of patients with end-stage cancer in hospice and their caregivers and to compare the scores for differences by age, gender, race, and computer use experience. INTERVENTION/METHODS: Immediately after using the tablet, 234 hospice patients and 231 caregivers independently completed the Computer Acceptability Scale (maximum scores of 14 for patients and 9 for caregivers). RESULTS: The mean (SD) Computer Acceptability score was 12.2 (1.9) for patients and 8.5 (0.9) for caregivers. Computer Acceptability scores were significantly associated with age and with previous computer use for both patients and caregivers. CONCLUSIONS: This technology was highly acceptable to patients and caregivers for reporting pain in real time to their hospice nurses. IMPLICATIONS FOR PRACTICE: Findings provide encouraging results that are worthy of serious consideration for patients who are in end stages of illness, including older persons and those with minimal computer experience. Increasing availability of technology can provide innovative methods for improving care provided to patients facing significant cancer-related pain even at the end of life.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".