Association of the Extent of Internet Use by Patients With Cancer With Social Support Among Patients and Change in Patient-Reported Treatment Outcomes During Inpatient Rehabilitation: Cross-sectional and Longitudinal Study
Bibliographic record
Abstract
Background Given the increasing number of cancer survivors and their rising survival rates, rehabilitation plays an increasingly important role. Social support among patients is an essential element of inpatient and day care rehabilitation. The internet can empower patients with cancer to become more active health care consumers and facilitate information and supportive care needs. By contrast, therapists suspect that high internet use during rehabilitation may severely limit social interactions between patients, thus interfering with the patients’ rehabilitation program and jeopardizing treatment success. Objective We hypothesized that the extent of internet use would be negatively related to social support among patients with cancer during their clinical stay as well as fewer improvements in patient-reported treatment outcomes from the first to the last day of their clinical stay. Methods Patients with cancer participated during their inpatient rehabilitation. Cross-sectional data, such as the extent of participants’ internet use and perceived social support among patients, were collected during the last week of their clinic stay. The treatment outcomes, that is, participants’ levels of distress, fatigue, and pain, were collected on the first and last day of the clinic stay. We used multiple linear regression analysis to study the association between the extent of internet use and social support among patients with cancer. We used linear mixed model analyses to study the association between the extent of internet use by patients with cancer and the change in patient-reported treatment outcomes. Results Of the 323 participants, 279 (86.4%) participants reported that they used the internet. The extent of the internet use (t315=0.78; P=.43) was not significantly associated with the perceived social support among the participants during their clinical stay. In addition, the extent of participants’ internet use during their clinical stay was not associated with changes in participants’ levels of distress (F1,299=0.12; P=.73), fatigue (F1,299=0.19; P=.67), and pain (F1,303=0.92; P=.34) from the first to the last day of their clinical stay. Conclusions The extent of internet use does not seem to be negatively associated with the perceived social support among patients with cancer or with the change in patients’ levels of distress, fatigue, or pain from the first to the last day of their clinical stay.
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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 | 0.000 | 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".