Designing Supportive e-Interventions for Partners of Men With Prostate Cancer Using Female Partners’ Experiences: Qualitative Exploration Study
Bibliographic record
Abstract
BACKGROUND: Partners of men living with prostate cancer (PCa) can experience a variety of unmet needs that are largely unaddressed by health care professionals. There is limited evidence to suggest which approach may be most effective in supporting partners' unmet needs and further research is required to determine how to provide support to caregivers and how technology solutions can be designed. OBJECTIVE: This study aims to explore the experience of partners of men living with PCa and their perceptions of the potential role of information technology in supporting their needs. METHODS: A qualitative descriptive methodology using focus groups and phone interviews was used. Purposive sampling was used to recruit people attending a national conference supported by a national PCa organization. Interview guides were adapted from an existing evidence-based smartphone app for caregivers of people with colorectal cancer. Sessions were audio recorded and transcribed verbatim. A coding framework was developed, and transcripts were coded line by line into the framework. Codes within the framework were grouped into descriptive categories that were then developed into analytical themes. RESULTS: A total of 17 female partners participated in the study, with an average age of 64 (SD 8.5) years. The following two main themes emerged: In the first theme, that is, How technology can be shaped to support female partners of prostate cancer survivors, the content and design of the smartphone app was discussed in addressing female partners' needs. The following four subthemes were developed: getting support from social networks and resources, the lack of relevant information, demystifying future care expectations during and following a PCa diagnosis, and delivering the smartphone app-to whom and from whom. In the second theme, that is, The benefits and barriers of technology, the suitability of smartphone apps as a supportive modality for female partners was described. This included three subthemes: the smartphone app as an appropriate modality for supporting female partners, the future anticipated benefits of using the smartphone app, and concerns for storing and accessing information on the internet. CONCLUSIONS: A smartphone app may be a suitable modality for providing information and peer support to female partners of men living with PCa. There is a need to provide peer support for female partners in future interventions to ensure that female partners' intimacy and daily practical needs are met.
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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) | 0.001 | 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".