Mobile Breast Cancer e-Support Program for Chinese Women With Breast Cancer Undergoing Chemotherapy (Part 1): Qualitative Study of Women’s Perceptions
Bibliographic record
Abstract
BACKGROUND: Women with breast cancer undergoing chemotherapy experience difficulty in accessing adequate cancer care in China. Mobile apps have the potential to provide easily accessible support for these women. However, there remains a paucity of randomized controlled trials to evaluate the effectiveness of app-based programs targeting specifically women with breast cancer undergoing chemotherapy. Moreover, women's perceptions and experiences related to using and interacting within the app-based program have rarely been reported. Therefore, an app-based Breast Cancer e-Support program was developed and evaluated using a randomized controlled trial. Based on the incorporation of Bandura's self-efficacy and social exchange theory, Breast Cancer e-Support program lasted for 12 weeks covering 4 cycles of chemotherapy and had 4 components: (1) a Learning forum, (2) a Discussion forum, (3) an Ask-the-Expert forum, and (4) a Personal Stories forum. OBJECTIVE: As a part of the randomized controlled trial, the aim of this study was to explore the participants' perception of Breast Cancer e-Support program, its strengths and weaknesses, and suggestions to improve the program. METHODS: A descriptive qualitative study was employed. Thirteen women with breast cancer from 2 university-affiliated hospitals in China, who were randomly allocated to the Breast Cancer e-Support program in the randomized controlled trial, were interviewed from November 2016 to February 2017. Purposive sampling was used based on women's scores of self-efficacy after the completion of the intervention. Inductive content analysis was used to analyze the transcripts, allowing the categories and subcategories to flow from the data. RESULTS: The qualitative interviews revealed that participants perceived the Breast Cancer e-Support program to be helpful in enhancing knowledge, improving confidence level, and promoting emotional well-being. Women also identified access to tailored advice from experts and convenience as the benefits of this program. Physical or psychological health status, stigma related with breast cancer, and app instability were mentioned as the challenges to engagement. Suggestions for improvement included adding message reminders to prompt instant communication and search engine to locate information quickly, supplementing more interesting and practical knowledge, updating the information more often, and quickening the responses to women's questions. The participants recommended the Breast Cancer e-Support program to be incorporated as routine care to support women during chemotherapy. CONCLUSIONS: This study demonstrates the potential of the Breast Cancer e-Support program to support women during chemotherapy. Future app-based programs should apply a family-centered approach and provide more support on stigma associated with the disease to encourage engagement with the app. Suggestions of improvement regarding the design, content, and operation of the app-based intervention should be addressed in future studies. It is promising to incorporate the Breast Cancer e-Support program into routine care to generalize the benefits. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12616000639426; http://www.ANZCTR.org.au/ ACTRN12616000639426.aspx (Archived by WebCite at http://www.webcitation.org/6v1n9hGZq).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".