The Participant Recruitment Outcomes (PRO) study: Exploring contemporary perspectives of telehealth trial non-participation through insights from patients, clinicians, study investigators, and study staff
Bibliographic record
Abstract
BACKGROUND: Telehealth has been proposed as an alternative means to providing traditional modes of care while alleviating the need for participant travel and reducing overall healthcare costs. The purpose of this study was to explore contemporary perspectives of patients and stakeholders regarding non-participation in telehealth trials. METHODS: We undertook a two-phase exploratory qualitative study to understand the reasons behind patient non-participation in telehealth. Data were collected through semi-structured interviews with non-participating patient participants (n = 8) and stakeholders (n = 27) including clinicians, study investigators, and study staff. An analysis of interview data were undertaken and guided by a qualitative descriptive approach. FINDINGS: Patients and stakeholders reported many barriers to telehealth participation including technological barriers, limited understanding of disease, and an understated need for services. Both groups had some overlap in their concerns but also provided unique insights. CONCLUSION: The analysis of study findings revealed perspectives of patients and stakeholders including barriers to participation as well as suggestions for future telehealth initiatives. Further research is needed to explore non-participation including patient readiness to assist in the development of future telehealth programs.
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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 | 0.164 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".