The Preferences of Patients With Cancer Regarding Apps to Help Meet Their Illness-Related Information Needs: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: The shift from inpatient to outpatient and community cancer care means that more patients with cancer need to manage their condition at home, without the direct supervision of their clinician. Subsequently, research has reported that many patients with cancer have unmet information needs during their illness. Mobile devices, such as mobile phones and tablet computers, provide an opportunity to deliver information to patients remotely. Before designing an app intervention to help patients with cancer to meet their information needs, in-depth qualitative research is required to gain an understanding of the views of the target users. OBJECTIVE: We aimed to develop an app intervention to help patients meet their illness-related information needs in noninpatient settings. This study explored the information needs of patients with cancer and their preferences for an app and desired app features. Specifically, the perceived acceptability of an app, desired app features, and the potential benefits and disadvantages of, and barriers to, an app were explored. METHODS: Qualitative, one-on-one semistructured interviews were conducted with patients with urological, colorectal, breast, or gynecological cancers (N=23) across two hospitals in South Wales. Interviews were audio-taped, transcribed, and analyzed using a thematic analysis. RESULTS: Findings indicated that barriers to information exchange and understanding in consultations, and identification of reliable information sources between consultations, appeared to contribute to patients' unmet information needs. Consequently, app feature suggestions included a question prompt list, a glossary of cancer terms, a resources feature, and a contacts feature. Anticipated benefits of this type of app included a more informed patient, improved quality of life, decreased anxiety, and increased confidence to participate in their care. The anticipated barriers to app use are likely to be temporary or can be minimized with regard to these findings during app development and implementation. CONCLUSIONS: This study highlights the desire of patients with cancer for an app intervention to help them meet their information needs during and between consultations with their clinicians. This study also highlights the anticipated acceptability and benefits of this type of intervention; however, further research is warranted.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".