Experiences With a Self-Reported Mobile Phone-Based System Among Patients With Colorectal Cancer: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: In cancer care, mobile phone-based systems are becoming more widely used in the assessment, monitoring, and management of side effects. OBJECTIVE: To explore the experiences of patients with colorectal cancer on using a mobile phone-based system for reporting neurotoxic side effects. METHODS: Eleven patients were interviewed (ages 44-68 years). A semistructured interview guide was used to perform telephone interviews. The interviews were transcribed verbatim and analyzed with qualitative content analysis. RESULTS: The patients' experiences of using a mobile phone-based system were identified and constructed as: "being involved," "pacing oneself," and "managing the questions." "Being involved" refers to their individual feelings. Patients were participating in their own care by being observant of the side effects they were experiencing. They were aware that the answers they gave were monitored in real time and taken into account by health care professionals when planning further treatment. "Pacing oneself" describes how the patients can have an impact on the time and place they choose to answer the questions. Answering the questionnaire was easy, and despite the substantial number of questions, it was quickly completed. "Managing the questions" pointed out that the patients needed to be observant because of the construction of the questions. They could not routinely answer all the questions. Patients understood that side effects can vary during the cycles of treatment and need to be assessed repeatedly during treatment. CONCLUSIONS: This mobile phone-based system reinforced the patients' feeling of involvement in their own care. The patients were comfortable with the technology and appreciated that the system was not time consuming.
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 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".