Using Telehealth to Train Providers of a Cancer Support Intervention
Bibliographic record
Abstract
BACKGROUND: Group interventions are effective for addressing the transition from cancer treatment to survivorship but are not widely available outside of urban areas. In addition, minimal training is available for group facilitators outside of the mental healthcare discipline. Telehealth as a medium can facilitate conversation and interactive learning and make learning accessible to individuals in areas that lack resources for traditional classroom teaching. Little is known, however, regarding the feasibility and acceptability of a telehealth training program for group leaders. This project aimed to investigate the utility of a telehealth training program for the delivery of a copyrighted, manualized psychosocial group intervention, Cancer Transitions: Moving Beyond Treatment. MATERIALS AND METHODS: Nine group leaders attended one in-person orientation, four telehealth training classes, and four telehealth supervision sessions, completing self-report measures of content knowledge, quality satisfaction, and self-confidence. Following the completion of their last Cancer Transitions facilitation, group leaders participated in a focus group to provide qualitative feedback regarding their experiences in training for and leading the respective groups in eight urban and rural North Carolina communities. RESULTS: Group leaders rated the training program highly across the domains of content knowledge, quality satisfaction, and self-confidence. Satisfaction with the technology itself was equivocal. CONCLUSIONS: Telehealth represents a feasible avenue for training and supporting leaders of psychosocial interventions. In addition, telehealth is particularly well suited to the need for training group leaders in areas outside urban centers or academic communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".