Acceptability and Usefulness of a Dyadic, Tailored, Web-Based, Psychosocial and Physical Activity Self-Management Program (TEMPO): A Qualitative Study
Bibliographic record
Abstract
Caregivers of men with prostate cancer report high burden, and there is a need to develop cost-effective programs to support them in their roles. This study reports on the acceptability of a dyadic, Tailored, wEb-based, psychosocial and physical activity (PA) self-Management PrOgram called TEMPO. TEMPO was accessed by a convenience sample of 19 men with prostate cancer and their caregivers (n = 18), as well as six health care professionals (HCPs). User feedback was gathered via semi-structured qualitative interviews. Data were analyzed using thematic analysis. Most dyads were satisfied with TEMPO, particularly with the dyadic feature of TEMPO, the focus on goal setting to integrate self-management, and the extensive health library. The patients and caregivers motivated each other as they worked through TEMPO. Most goals to achieve during TEMPO pertained to increasing PA, followed by learning physical symptom management. One unanticipated benefit of TEMPO for the dyads was improved communication. HCPs agreed that TEMPO was a novel approach to online cancer self-management and they echoed the benefits reported by dyads. Key suggestions for improving TEMPO were to reduce repetition, tailor content, add more exercise ideas, and have more printing options. This study provides a strong foundation on which to plan a larger trial.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".