Perceptions of clinicians and older adults regarding telehealth in neuropsychology and speech-language pathology: a qualitative study
Bibliographic record
Abstract
Abstract Introduction With a growing proportion of older adults at increased risk of cognitive impairments requiring neuropsychological or speech-language pathology services, telehealth has emerged as an effective solution to overcome barriers to healthcare access, particularly highlighted during the COVID-19 pandemic. To understand the ongoing challenges and opportunities in a post-pandemic context, this study aimed to describe telehealth-related factors in neuropsychology and speech-language pathology for older adults across diverse clinical and regional settings in Québec, Canada, integrating input from both clinicians and older adults. Methods Focus groups were conducted with 11 speech-language pathologists (three groups), 9 neuropsychologists (two groups), and 17 older adults (four groups) to explore their perceptions on telehealth-based activities in neuropsychology and speech-language pathology. Focus group discussions were transcribed and analyzed using an inductive approach, comparing results across all participant groups. Results According to the participants, telehealth can be a solution to access problems in neuropsychology and speech-language pathology. However, participants mentioned the lack of resource availability as a significant barrier to telehealth use. Additionally, patient-specific characteristics may hinder some older adults from benefiting from this modality. Discussion Telehealth in neuropsychology and speech-language pathology appears acceptable and feasible to participants, provided that the virtual modality is accessible to patients and that they are included in the decision-making process. Furthermore, clinicians should be able to rely on more comprehensive validation data for clinical activities, and an effort should be made to make telehealth more accessible to older adults living in rural areas. Future research should explore specific hybrid models to mitigate potential telehealth barriers.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".