The Cancer Patient’s Use and Appreciation of the Internet and Other Modern Means of Communication
Bibliographic record
Abstract
As computers and smartphones continue to transform the doctor-patient relationship, it is essential that healthcare professionals understand how their patients wish to interact with these devices. The results from a satisfaction questionnaire of 225 Oncology patients treated in 2011 in Quebec, Canada provide insight into the manner in which patients have been and wish to communicate with their healthcare teams. The survey also addressed whether or not patients searched the Internet for supplementary information regarding their condition. Generally, patients were neutral regarding adopting greater usage of modern means of communication. The majority of patients did not want to be contacted via e-mail or SMS, nor did the patients want to make appointments or fill out surveys online. Forty four percent of patients used the Internet to learn more about their condition. Concerning the patients who were not provided with links to medically relevant websites, 44% wished their doctors had supplied them with such links. Though there was much overlap between the 44% of patients who went on the Internet to learn more about their condition and the 44% of the patients who wished their physicians provided them with such links, 14% of all the patients wished their medical teams had provided them with links, but did not independently search for medically relevant information about their condition. Using chi-square testing education level was found to be the best predictor of which patients searched the web for supplementary information about their conditions (p = 0.003). Contrary to findings in other studies, a comparable proportion of patients in each age-group used the Internet to research their condition. Given the wealth of web-resources available to cancer patients, it would be beneficial for both healthcare teams and their patients if physicians consistently offered a list of trustworthy websites to their patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".