Role of telehealth in seating clinics: a case study of learners' perspectives
Bibliographic record
Abstract
We conducted a qualitative case study of the introduction of telehealth in a seating clinic, which was an existing outreach service between two hospitals in Alberta, approximately 300 km apart. Interviews were conducted with the staff who were involved in planning and implementing the telehealth initiative. The study showed that, from the perspective of the staff (who were learners), implementation of telehealth in seating clinics differs from other less tactile telehealth applications in certain ways: (1) the importance of multidisciplinary teams in the procedures, (2) the importance of proper visualization and communication among the staff to convey the pressure changes and measurements to the technicians at the major centre to help them build or adjust the seating devices and (3) the reluctance of staff to trust others' judgements. Planning of service provision and telelearning for seating clinics requires the involvement of staff at all stages. Thus, the implementation of telehealth should be a stepwise process, allowing a highly interactive approach, without affecting the multidisciplinary nature of seating clinics.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".