Evaluation of Telehealth for Preclinic Assessment and Follow-Up in an Interprofessional Rural and Remote Memory Clinic
Bibliographic record
Abstract
Using data from a sample of 169 patients, this study evaluates the acceptability and feasibility of telehealth videoconferencing for preclinic assessment and follow-up in an interprofessional memory clinic for rural and remote seniors. Patients and caregivers are seen via telehealth prior to the in-person clinic, and followed at 6 weeks, 12 weeks, 6 months, one year, and yearly. Patients are randomly assigned to in-person (standard care) or telehealth for the first follow-up, then alternating between the two modes of treatment, prior to 1-year follow-up. On average, telehealth appointments reduce participants' travel by 426 km per round trip. Findings show that telehealth coordinators rated 85% of patients and 92% of caregiversas comfortable or very comfortable during telehealth. Satisfaction scales completed by patient-caregiver dyads show high satisfaction with telehealth. Follow-up questionnaires reveal similar satisfaction with telehealth and in-person appointments, but telehealth is rated as significantly more convenient. Predictors of discontinuing follow-up are greater distance to telehealth, old-age patient, lower telehealth satisfaction, and lower caregiver burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".