Evaluating the experience of rural individuals with prostate and breast cancer participating in research via telehealth
Bibliographic record
Abstract
INTRODUCTION: Studies that use objective assessments often only recruit individuals in the geographic region in which the study is being conducted, because the assessments require that the researcher and participant be face to face. This limits the number and variety of individuals who can participate. Telehealth is one approach that could be used to increase sample size and representativeness. The present analysis aims to evaluate the experience of individuals diagnosed with breast or prostate cancer, who participated by telehealth in studies investigating the effects of cancer treatment on sleep and cognition. Specifically, this study aimed to highlight potential benefits of using telehealth and identify ways to improve the process for future studies and assessments. METHODS: Telephone interviews were conducted with 20 individuals with cancer who participated via telehealth in a larger study investigating the effects of cancer treatment on sleep and cognition; 12 individuals had breast cancer and 8 individuals had prostate cancer. Participants were organized into the four regional health authorities of Newfoundland and Labrador: Eastern, Western, Central, and Grenfell-Labrador. Participants of varying ages and communities were purposively selected. Participants were interviewed about their experience participating in the study via telehealth and invited to offer suggestions for how to improve the process. Interview transcripts were coded using a thematic analysis approach. Demographic information was used to characterize the sample. RESULTS: Including telehealth as an option in the overall study allowed for a 55% sample size increase for participants with breast cancer, and a 45% sample size increase for participants with prostate cancer. Participants reported an overall positive experience (70% reported the experience as good and/or great), with telehealth allowing for greater convenience, more personable interactions, increased access, and an otherwise unavailable opportunity to help others and themselves. Identified areas for improvement were sound quality, and better access for those who still face barriers of commuting to telehealth locations. Inter-rater reliability yielded a 92% agreement. CONCLUSIONS: For studies and assessments requiring face-to-face contact, telehealth is clearly a feasible option for improving research representativeness and access for individuals residing in rural areas. Future research should make use of telehealth services, to give a voice to rural individuals who are too often left out.
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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 | 0.006 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".